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The purpose of action prediction is to recognize an action before it is completed to reduce recognition latency. Because action prediction has lower latency than action recognition, it can be applied to a variety of surveillance scenarios and responds faster. However, action prediction is more difficult because it cannot obtain the complete action execution. In this paper, we study the action prediction which is based on skeleton data and propose a new network called adaptive graph convolutional network with adversarial learning (AGCN-AL) for it. The AGCN-AL uses adversarial learning to make the features of the partial sequences as similar as possible to the features of the full sequences to learn the potential global information in the partial sequences. Besides, partial sequences with different numbers of frames contain different amounts of information. We introduce temporal-dependent loss functions to prevent the network from paying too much attention to partial sequences whose observation ratios are small, and ignoring partial sequences whose observation ratios are large. Moreover, the AGCN-AL is combined with the local AGCN into a two-stream network to enhance the prediction, proving that the local information and the potential global information in partial sequences are complementary. We evaluate the proposed approach on two datasets and show excellent performance.
Guangxin Li; Nanjun Li; Faliang Chang; Chunsheng Liu. Adaptive Graph Convolutional Network with Adversarial Learning for Skeleton-Based Action Prediction. IEEE Transactions on Cognitive and Developmental Systems 2021, PP, 1 -1.
AMA StyleGuangxin Li, Nanjun Li, Faliang Chang, Chunsheng Liu. Adaptive Graph Convolutional Network with Adversarial Learning for Skeleton-Based Action Prediction. IEEE Transactions on Cognitive and Developmental Systems. 2021; PP (99):1-1.
Chicago/Turabian StyleGuangxin Li; Nanjun Li; Faliang Chang; Chunsheng Liu. 2021. "Adaptive Graph Convolutional Network with Adversarial Learning for Skeleton-Based Action Prediction." IEEE Transactions on Cognitive and Developmental Systems PP, no. 99: 1-1.
Rotating machines are one of the most common equipment in modern industry, the health condition of the equipment is closely linked to safety of workers and production effectiveness. Thus accurate and robust fault diagnostic approaches are vital to safety production. In practice, diagnostic accuracy is seriously affected by noises, especially in low signal-to-noise (SNR) ratio conditions, and the quality of fault features is positively link to the diagnosing accuracy. In consideration of distinguishable feature expression can improve diagnosing result and robust to wider range of experimental conditions, this paper presents a novel spectrogram based local fluctuation feature (SLFF) for low SNR conditions. Firstly, signals are transformed into spectrograms. Then a feature extracting window bank is established on spectrograms for SLFF. At last, a support vector machine (SVM) is applied as a fault classifier for evaluating the proposed feature. The proposed SLFF represents the basic spectral shape and variation which leads to robust and well distinguishable feature expression, the feature reveals the differences of spectral local variation trends between normal and fault types that can improve the discrimination under the influence of strong noises. The effectiveness of the proposed method has been proved experimentally in this paper.
Qinyu Jiang; Faliang Chang; Chunsheng Liu. A Spectrogram Based Local Fluctuation Feature for Fault Diagnosis with Application to Rotating Machines. Journal of Electrical Engineering & Technology 2021, 16, 2167 -2181.
AMA StyleQinyu Jiang, Faliang Chang, Chunsheng Liu. A Spectrogram Based Local Fluctuation Feature for Fault Diagnosis with Application to Rotating Machines. Journal of Electrical Engineering & Technology. 2021; 16 (4):2167-2181.
Chicago/Turabian StyleQinyu Jiang; Faliang Chang; Chunsheng Liu. 2021. "A Spectrogram Based Local Fluctuation Feature for Fault Diagnosis with Application to Rotating Machines." Journal of Electrical Engineering & Technology 16, no. 4: 2167-2181.
An adversarial reinforced report-generation framework for chest x-ray images is proposed. Previous medical-report-generation models are mostly trained by minimizing the cross-entropy loss or further optimizing the common image-captioning metrics, such as CIDEr, ignoring diagnostic accuracy, which should be the first consideration in this area. Inspired by the generative adversarial network, an adversarial reinforcement learning approach is proposed for report generation of chest x-ray images considering both diagnostic accuracy and language fluency. Specifically, an accuracy discriminator (AD) and fluency discriminator (FD) are built that serve as the evaluators by which a report based on these two aspects is scored. The FD checks how likely a report originates from a human expert, while the AD determines how much a report covers the key chest observations. The weighted score is viewed as a “reward” used for training the report generator via reinforcement learning, which solves the problem that the gradient cannot be passed back to the generative model when the output is discrete. Simultaneously, these two discriminators are optimized by maximum-likelihood estimation for better assessment ability. Additionally, a multi-type medical concept fused encoder followed by a hierarchical decoder is adopted as the report generator. Experiments on two large radiograph datasets demonstrate that the proposed model outperforms all methods to which it is compared.
Daibing Hou; Zijian Zhao; Yuying Liu; Faliang Chang; SanYuan Hu. Automatic Report Generation for Chest X-Ray Images via Adversarial Reinforcement Learning. IEEE Access 2021, 9, 21236 -21250.
AMA StyleDaibing Hou, Zijian Zhao, Yuying Liu, Faliang Chang, SanYuan Hu. Automatic Report Generation for Chest X-Ray Images via Adversarial Reinforcement Learning. IEEE Access. 2021; 9 ():21236-21250.
Chicago/Turabian StyleDaibing Hou; Zijian Zhao; Yuying Liu; Faliang Chang; SanYuan Hu. 2021. "Automatic Report Generation for Chest X-Ray Images via Adversarial Reinforcement Learning." IEEE Access 9, no. : 21236-21250.
It can be a very challenging task when using level set method segmenting natural images with high intensity inhomogeneity and complex background scenes. A new synthesis level set method for robust image segmentation based on the combination of Retinex‐corrected saliency region information and edge information is proposed in this work. First, the Retinex theory is introduced to correct the saliency information extraction. Second, the Retinex‐corrected saliency information is embedded into the level set method due to its advantageous quality which makes a foreground object stand out relative to the backgrounds. Combined with the edge information, the boundary of segmentation will be more precise and smooth. Experiments indicate that the proposed segmentation algorithm is efficient, fast, reliable, and robust.
Dongmei Liu; Faliang Chang; Huaxiang Zhang; Li Liu. Level set method with Retinex‐corrected saliency embedded for image segmentation. IET Image Processing 2021, 15, 1530 -1541.
AMA StyleDongmei Liu, Faliang Chang, Huaxiang Zhang, Li Liu. Level set method with Retinex‐corrected saliency embedded for image segmentation. IET Image Processing. 2021; 15 (7):1530-1541.
Chicago/Turabian StyleDongmei Liu; Faliang Chang; Huaxiang Zhang; Li Liu. 2021. "Level set method with Retinex‐corrected saliency embedded for image segmentation." IET Image Processing 15, no. 7: 1530-1541.
This paper proposes an intermediate fused network with multiple timescales to predict future video segments for video anomaly detection. Video prediction technique for anomaly detection requires to derive an anomaly-distinguishable future frame from normal distributions provided by training data. Then by measuring the difference between generated frames and reference frames, the model can tell which frames represent anomalous events in certain video sequence. In order to synthesize more distinctive future frames, we propose to boost image predictions by integrating several input video segments in multiple timescales. Specifically, the diversity in timescales means the diversity in sampling frequencies of video frames. In addition, we introduce a novel intermediate fusion strategy by concatenating feature maps from intermediate layers to better preserve different characteristics from different timescales. We evaluate the proposed method on some public video surveillance datasets and achieve competitive results with respect to the state-of-the-art approaches.
Wenqian Wang; Faliang Chang; Huadong Mi. Intermediate fused network with multiple timescales for anomaly detection. Neurocomputing 2020, 433, 37 -49.
AMA StyleWenqian Wang, Faliang Chang, Huadong Mi. Intermediate fused network with multiple timescales for anomaly detection. Neurocomputing. 2020; 433 ():37-49.
Chicago/Turabian StyleWenqian Wang; Faliang Chang; Huadong Mi. 2020. "Intermediate fused network with multiple timescales for anomaly detection." Neurocomputing 433, no. : 37-49.
To enhance surgeons’ efficiency and safety of patients, minimally invasive surgery (MIS) is widely used in a variety of clinical surgeries. Real-time surgical tool detection plays an important role in MIS. However, most methods of surgical tool detection may not achieve a good trade-off between detection speed and accuracy. We propose a real-time attention-guided convolutional neural network (CNN) for frame-by-frame detection of surgical tools in MIS videos, which comprises a coarse (CDM) and a refined (RDM) detection modules. The CDM is used to coarsely regress the parameters of locations to get the refined anchors and perform binary classification, which determines whether the anchor is a tool or background. The RDM subtly incorporates the attention mechanism to generate accurate detection results utilizing the refined anchors from CDM. Finally, a light-head module for more efficient surgical tool detection is proposed. The proposed method is compared to eight state-of-the-art detection algorithms using two public (EndoVis Challenge and ATLAS Dione) datasets and a new dataset we introduced (Cholec80-locations), which extends the Cholec80 dataset with spatial annotations of surgical tools. Our approach runs in real-time at 55.5 FPS and achieves 100, 94.05, and 91.65% mAP for the above three datasets, respectively. Our method achieves accurate, fast, and robust detection results by end-to-end training in MIS videos. The results demonstrate the effectiveness and superiority of our method over the eight state-of-the-art methods.
Pan Shi; Zijian Zhao; SanYuan Hu; Faliang Chang. Real-Time Surgical Tool Detection in Minimally Invasive Surgery Based on Attention-Guided Convolutional Neural Network. IEEE Access 2020, 8, 228853 -228862.
AMA StylePan Shi, Zijian Zhao, SanYuan Hu, Faliang Chang. Real-Time Surgical Tool Detection in Minimally Invasive Surgery Based on Attention-Guided Convolutional Neural Network. IEEE Access. 2020; 8 ():228853-228862.
Chicago/Turabian StylePan Shi; Zijian Zhao; SanYuan Hu; Faliang Chang. 2020. "Real-Time Surgical Tool Detection in Minimally Invasive Surgery Based on Attention-Guided Convolutional Neural Network." IEEE Access 8, no. : 228853-228862.
Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APO-AIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper.
Qinyu Jiang; Faliang Chang. A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection. Journal of Mechanical Science and Technology 2020, 34, 1 -10.
AMA StyleQinyu Jiang, Faliang Chang. A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection. Journal of Mechanical Science and Technology. 2020; 34 (9):1-10.
Chicago/Turabian StyleQinyu Jiang; Faliang Chang. 2020. "A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection." Journal of Mechanical Science and Technology 34, no. 9: 1-10.
Machine vision based vehicle counting and traffic flow estimation are challenging problems especially for dense traffic scenarios. Previous line of interest (LOI) counting methods rarely focus on dense scenarios and their performance largely relies on the accuracy of tracking. Avoiding the use of complex tracking methods, an LOI counting framework is proposed to address the bi-directional LOI counting problem in dense scenarios. There are three main contributions. Firstly, instead of treating the LOI vehicle counting problem as a combination of detecting and tracking of individual vehicles, the bi-directional traffic flow is taken as a whole and a novel spatio-temporal counting feature (STCF) is proposed for extracting bi-directional traffic flow features in dense traffic scenarios. Secondly, without relying on a multi-target tracking process for tracking and counting each vehicle, a counting network is proposed, called the counting Long Short-Term Memory (cLSTM) network, to do analysis of the bi-directional STCF features and vehicle counting in successive video frames. Lastly, an estimation model is designed for estimating traffic flow parameters including speed, volume and density. Experiments performed on the UA-DETRAC dataset and the captured videos show that the proposed vehicle counting method outperforms the tested representative LOI counting methods in both accuracy and speed, and that the proposed framework can efficiently estimate traffic flow parameters including speed, volume and density in real time.
Shuang Li; Faliang Chang; Chunsheng Liu. Bi-Directional Dense Traffic Counting Based on Spatio-Temporal Counting Feature and Counting-LSTM Network. IEEE Transactions on Intelligent Transportation Systems 2020, 1 -13.
AMA StyleShuang Li, Faliang Chang, Chunsheng Liu. Bi-Directional Dense Traffic Counting Based on Spatio-Temporal Counting Feature and Counting-LSTM Network. IEEE Transactions on Intelligent Transportation Systems. 2020; (99):1-13.
Chicago/Turabian StyleShuang Li; Faliang Chang; Chunsheng Liu. 2020. "Bi-Directional Dense Traffic Counting Based on Spatio-Temporal Counting Feature and Counting-LSTM Network." IEEE Transactions on Intelligent Transportation Systems , no. 99: 1-13.
With the advantage of high maneuverability, Unmanned Aerial Vehicles (UAVs) have been widely deployed in vehicle monitoring and controlling. However, processing the images captured by UAV for the extracting vehicle information is hindered by some challenges including arbitrary orientations, huge scale variations and partial occlusion. In seeking to address these challenges, we propose a novel Multi-Scale and Occlusion Aware Network (MSOA-Net) for UAV based vehicle segmentation, which consists of two parts including a Multi-Scale Feature Adaptive Fusion Network (MSFAF-Net) and a Regional Attention based Triple Head Network (RATH-Net). In MSFAF-Net, a self-adaptive feature fusion module is proposed, which can adaptively aggregate hierarchical feature maps from multiple levels to help Feature Pyramid Network (FPN) deal with the scale change of vehicles. The RATH-Net with a self-attention mechanism is proposed to guide the location-sensitive sub-networks to enhance the vehicle of interest and suppress background noise caused by occlusions. In this study, we release a large comprehensive UAV based vehicle segmentation dataset (UVSD), which is the first public dataset for UAV based vehicle detection and segmentation. Experiments are conducted on the challenging UVSD dataset. Experimental results show that the proposed method is efficient in detecting and segmenting vehicles, and outperforms the compared state-of-the-art works.
Wang Zhang; Chunsheng Liu; Faliang Chang; Ye Song. Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images. Remote Sensing 2020, 12, 1760 .
AMA StyleWang Zhang, Chunsheng Liu, Faliang Chang, Ye Song. Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images. Remote Sensing. 2020; 12 (11):1760.
Chicago/Turabian StyleWang Zhang; Chunsheng Liu; Faliang Chang; Ye Song. 2020. "Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images." Remote Sensing 12, no. 11: 1760.
Time-efficient anomaly detection and localization in video surveillance still remains challenging due to the complexity of "anomaly". In this paper, we propose a cuboid-patch-based method characterized by a cascade of classifiers called a spatialtemporal cascade autoencoder (ST-CaAE), which makes full use of both spatial and temporal cues from video data. The ST-CaAE has two main stages, defined by two proposed neural networks: a spatial-temporal adversarial autoencoder (ST-AAE) and a spatial-temporal convolutional autoencoder (ST-CAE). First, the ST-AAE is used to preliminarily identify anomalous video cuboids and exclude normal cuboids. The key idea underlying ST-AAE is to obtain a Gaussian model to fit the distribution of the regular data. Then in the second stage, the ST-CAE classifies the specific abnormal patches in each anomalous cuboid with reconstruction error based strategy that takes advantage of the CAE and skip connection. A two-stream framework is utilized to fuse the appearance and motion cues to achieve more complete detection results, taking the gradient and optical flow cuboids as inputs for each stream. The proposed ST-CaAE is evaluated using three public datasets. The experimental results verify that our framework outperforms other state-of-the-art works.
Nanjun Li; Faliang Chang; Chunsheng Liu. Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes. IEEE Transactions on Multimedia 2020, 23, 203 -215.
AMA StyleNanjun Li, Faliang Chang, Chunsheng Liu. Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes. IEEE Transactions on Multimedia. 2020; 23 (99):203-215.
Chicago/Turabian StyleNanjun Li; Faliang Chang; Chunsheng Liu. 2020. "Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes." IEEE Transactions on Multimedia 23, no. 99: 203-215.
Youmei Zhang; Chunluan Zhou; Faliang Chang; Alex C. Kot. A scale adaptive network for crowd counting. Neurocomputing 2019, 362, 139 -146.
AMA StyleYoumei Zhang, Chunluan Zhou, Faliang Chang, Alex C. Kot. A scale adaptive network for crowd counting. Neurocomputing. 2019; 362 ():139-146.
Chicago/Turabian StyleYoumei Zhang; Chunluan Zhou; Faliang Chang; Alex C. Kot. 2019. "A scale adaptive network for crowd counting." Neurocomputing 362, no. : 139-146.
In this paper, we present a novel deep learning based method for video anomaly detection and localization. The key idea of our approach is that the latent space representations of normal samples are trained to accord with a specific prior distribution by the proposed deep neural network - Multivariate Gaussian Fully Convolution Adversarial Autoencoder (MGFC-AAE), while the latent representations of anomalies do not. In order to extract deep features from input samples as latent representations, a convolutional neural network (CNN) is employed for the encoder of the deep network. Based on the probability that the test sample is associated with the prior distribution, an energy-based method is applied to obtain its anomaly score. A two-stream framework is utilized to integrate the appearance and motion cues to achieve more comprehensive detection results, taking the gradient and optical flow patches as inputs for each stream. Besides, a multi-scale patch structure is put forward to handle the perspective of some video scenes. Experiments are conducted on three public datasets, results verify that our framework can accurately detect and locate abnormal objects in various video scenes, achieving competitive performance when compared with other state-of-the-art works.
Nanjun Li; Faliang Chang. Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder. Neurocomputing 2019, 369, 92 -105.
AMA StyleNanjun Li, Faliang Chang. Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder. Neurocomputing. 2019; 369 ():92-105.
Chicago/Turabian StyleNanjun Li; Faliang Chang. 2019. "Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder." Neurocomputing 369, no. : 92-105.
Sound source localization is one of the major audiovisual functions of intelligent robots. With the development of computer technology, high-quality sound signal acquisition technology has been widely used in many fields such as microphone array sound source localization. Support Vector Machine (SVM) is a kind of machine learning based on statistical learning theory and structural risk minimization principle. Many parameters will affect the performance of SVM. By changing parameters, the ability of anti-noise can be improved. However it comes at the expense of computation and speed. Proximal support vector machine (PSVM) is an improvement on classical SVM. It has smaller amount of calculation and faster speed. In this paper, by extracting the characteristics of generalized cross-correlation function of sound source signal and using PSVM to locate the sound source, the sound source localization accuracy is better in the reverberation and noise environment, and it has good robust performance.
Bowen Sheng; Qinyu Jiang; Faliang Chang. Sound Source Localization Based on PSVM algorithm. Algorithms and Data Structures 2019, 585 -593.
AMA StyleBowen Sheng, Qinyu Jiang, Faliang Chang. Sound Source Localization Based on PSVM algorithm. Algorithms and Data Structures. 2019; ():585-593.
Chicago/Turabian StyleBowen Sheng; Qinyu Jiang; Faliang Chang. 2019. "Sound Source Localization Based on PSVM algorithm." Algorithms and Data Structures , no. : 585-593.
You Only Look Once (YOLO) deep network can detect objects quickly with high precision and has been successfully applied in many detection problems. The main shortcoming of YOLO network is that YOLO network usually cannot achieve high precision when dealing with small-size object detection in high resolution images. To overcome this problem, we propose an effective region proposal extraction method for YOLO network to constitute an entire detection structure named ACF-PR-YOLO, and take the cyclist detection problem to show our methods. Instead of directly using the generated region proposals for classification or regression like most region proposal methods do, we generate large-size potential regions containing objects for the following deep network. The proposed ACF-PR-YOLO structure includes three main parts. Firstly, a region proposal extraction method based on aggregated channel feature (ACF) is proposed, called ACF based region proposal (ACF-PR) method. In ACF-PR, ACF is firstly utilized to fast extract candidates and then a bounding boxes merging and extending method is designed to merge the bounding boxes into correct region proposals for the following YOLO net. Secondly, we design suitable YOLO net for fine detection in the region proposals generated by ACF-PR. Lastly, we design a post-processing step, in which the results of YOLO net are mapped into the original image outputting the detection and localization results. Experiments performed on the Tsinghua-Daimler Cyclist Benchmark with high resolution images and complex scenes show that the proposed method outperforms the other tested representative detection methods in average precision, and that it outperforms YOLOv3 by 13.69 % average precision and outperforms SSD by 25.27 % average precision.
Chunsheng Liu; Yu Guo; Shuang Li; Faliang Chang. ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution Images. Sensors 2019, 19, 2671 .
AMA StyleChunsheng Liu, Yu Guo, Shuang Li, Faliang Chang. ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution Images. Sensors. 2019; 19 (12):2671.
Chicago/Turabian StyleChunsheng Liu; Yu Guo; Shuang Li; Faliang Chang. 2019. "ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution Images." Sensors 19, no. 12: 2671.
Rolling-element bearings (REBs) faults are one of the most common breakdowns of rotating machines, thus proposing effective bearing fault diagnosis and classification methods is vital. In previous studies, lots of bearing fault classification methods have been proposed to solve the problem in low signal-to-noise ratio (SNR) conditions. Though satisfactory classification results have been obtained, in consideration of the practicability and application scenarios, there are still many aspects to improve, such as the complexity of method and the classification ability in lower SNR conditions. Therefore, this paper presents a novel method that combines lower-order moment spectrum with support vector machine (SVM) for bearing fault classification in low SNR conditions. The lower-order moment spectrum reduces influence of Gaussian noise and enhances the quality of fault feature. A bandpass filter group (BPFG) has been used to reduce the dimension of the lower-order moment spectra (LOMS) as feature vectors. And a following SVM has been applied as the fault classifier, due to the mature application and satisfactory performance in fault classification. The proposed method is demonstrated to have strong ability of classification in low SNR conditions experimentally.
Qinyu Jiang; Faliang Chang. A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine. Journal of Mechanical Science and Technology 2019, 33, 1535 -1543.
AMA StyleQinyu Jiang, Faliang Chang. A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine. Journal of Mechanical Science and Technology. 2019; 33 (4):1535-1543.
Chicago/Turabian StyleQinyu Jiang; Faliang Chang. 2019. "A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine." Journal of Mechanical Science and Technology 33, no. 4: 1535-1543.
Estimating crowd counts remains a challenging task due to the problems of scale variations, non-uniform distribution and complex backgrounds. In this paper, we propose a multi-resolution attention convolutional neural network (MRA-CNN) to address this challenging task. Except for the counting task, we exploit an additional density-level classification task during training and combine features learned for the two tasks, thus forming multi-scale, multi-contextual features to cope with the scale variation and non-uniform distribution. Besides, we utilize a multi-resolution attention (MRA) model to generate score maps, where head locations are with higher scores to guide the network to focus on head regions and suppress non-head regions regardless of the complex backgrounds. During the generation of score maps, atrous convolution layers are used to expand the receptive field with fewer parameters, thus getting higher-level features and providing the MRA model more comprehensive information. Experiments on ShanghaiTech, WorldExpo’10 and UCF datasets demonstrate the effectiveness of our method.
Youmei Zhang; Chunluan Zhou; Faliang Chang; Alex C. Kot. Multi-resolution attention convolutional neural network for crowd counting. Neurocomputing 2018, 329, 144 -152.
AMA StyleYoumei Zhang, Chunluan Zhou, Faliang Chang, Alex C. Kot. Multi-resolution attention convolutional neural network for crowd counting. Neurocomputing. 2018; 329 ():144-152.
Chicago/Turabian StyleYoumei Zhang; Chunluan Zhou; Faliang Chang; Alex C. Kot. 2018. "Multi-resolution attention convolutional neural network for crowd counting." Neurocomputing 329, no. : 144-152.
Multi-target tracking in complex scenes is challenging because target appearance features generate partial or significant variations frequently. In order to solve the problem, we propose a multi-target tracking method with hierarchical data association using main-parts and spatial-temporal feature models. In our tracking framework, target feature models and tracklets are initialized when the new targets appear. Main-parts feature model is presented to represent target with partial or no appearance variations. It is established by partitioning a target template into several parts and formulating appearance variation densities of these parts. For the target with significant appearance variations, the tracker learns its global spatial-temporal feature model by integrating appearance with histogram of optical flow features. During tracking, tracklet confidence is exploited to implement hierarchical data association. According to different tracklet confidence values, main-parts and global data association are respectively performed by employing main-parts and spatial-temporal feature models. As a result, our approach uses the Hungarian algorithm to obtain optimal associated pairs between target tracklets and detections. Finally, target feature models and tracklets are updated by the association detections for subsequently tracking. Experiments conducted on CAVIAR, Parking Lot and MOT15 datasets verify the effectiveness and improvement of our multi-target tracking method.
HongBin Liu; Faliang Chang; Chunsheng Liu. Multi-target tracking with hierarchical data association using main-parts and spatial-temporal feature models. Multimedia Tools and Applications 2018, 78, 29161 -29181.
AMA StyleHongBin Liu, Faliang Chang, Chunsheng Liu. Multi-target tracking with hierarchical data association using main-parts and spatial-temporal feature models. Multimedia Tools and Applications. 2018; 78 (20):29161-29181.
Chicago/Turabian StyleHongBin Liu; Faliang Chang; Chunsheng Liu. 2018. "Multi-target tracking with hierarchical data association using main-parts and spatial-temporal feature models." Multimedia Tools and Applications 78, no. 20: 29161-29181.
Though license plate detection has been successfully applied in some commercial products, the detection of small and vague license plates in real applications is still an open problem. In this paper, we propose a novel hybrid cascade structure for fast detecting small and vague license plates in large and complex visual surveillance scenes. For rapid license plate candidate extraction, we propose two cascade detectors, including the Cascaded Color Space Transformation of Pixel detector and the Cascaded Contrast-Color Haar-like detector; these two cascade detectors can do coarse-to-fine detection in the front and in the middle of the hybrid cascade. In the end of the hybrid cascade, we propose a cascaded convolutional network structure (Cascaded ConvNet), including two detection-ConvNets and a calibration-ConvNet, which is designed to do fine detection. Through experiments with different evaluation data sets with many small and vague plates, we show that the proposed framework is able to rapidly detect license plates with different resolutions and different sizes in large and complex visual surveillance scenes.
Chunsheng Liu; Faliang Chang. Hybrid Cascade Structure for License Plate Detection in Large Visual Surveillance Scenes. IEEE Transactions on Intelligent Transportation Systems 2018, 20, 2122 -2135.
AMA StyleChunsheng Liu, Faliang Chang. Hybrid Cascade Structure for License Plate Detection in Large Visual Surveillance Scenes. IEEE Transactions on Intelligent Transportation Systems. 2018; 20 (6):2122-2135.
Chicago/Turabian StyleChunsheng Liu; Faliang Chang. 2018. "Hybrid Cascade Structure for License Plate Detection in Large Visual Surveillance Scenes." IEEE Transactions on Intelligent Transportation Systems 20, no. 6: 2122-2135.
With rapid calculation speed and relatively high accuracy, the AdaBoost-based detection framework has been successfully applied in some real applications of machine vision-based intelligent systems. The main shortcoming of the AdaBoost-based detection framework is that the off-line trained detector cannot be transfer retrained to adapt to unknown application scenes. In this paper, a new transfer learning structure based on two novel methods of supplemental boosting and cascaded ConvNet is proposed to address this shortcoming. The supplemental boosting method is proposed to supplementally retrain an AdaBoost-based detector for the purpose of transferring a detector to adapt to unknown application scenes. The cascaded ConvNet is designed and attached to the end of the AdaBoost-based detector for improving the detection rate and collecting supplemental training samples. With the added supplemental training samples provided by the cascaded ConvNet, the AdaBoost-based detector can be retrained with the supplemental boosting method. The detector combined with the retrained boosted detector and cascaded ConvNet detector can achieve high accuracy and a short detection time. As a representative object detection problem in intelligent transportation systems, the traffic sign detection problem is chosen to show our method. Through experiments with the public datasets from different countries, we show that the proposed framework can quickly detect objects in unknown application scenes.
Chunsheng Liu; Shuang Li; Faliang Chang; Wenhui Dong. Supplemental Boosting and Cascaded ConvNet Based Transfer Learning Structure for Fast Traffic Sign Detection in Unknown Application Scenes. Sensors 2018, 18, 2386 .
AMA StyleChunsheng Liu, Shuang Li, Faliang Chang, Wenhui Dong. Supplemental Boosting and Cascaded ConvNet Based Transfer Learning Structure for Fast Traffic Sign Detection in Unknown Application Scenes. Sensors. 2018; 18 (7):2386.
Chicago/Turabian StyleChunsheng Liu; Shuang Li; Faliang Chang; Wenhui Dong. 2018. "Supplemental Boosting and Cascaded ConvNet Based Transfer Learning Structure for Fast Traffic Sign Detection in Unknown Application Scenes." Sensors 18, no. 7: 2386.
Multitarget tracking in video surveillance is challenging because the appearance features of the target are often unreliable in complicated scenes. To solve the problem, we propose a multitarget tracking method using multifeature model with acceleration feature. First, an acceleration feature descriptor is derived from the histograms of the optical flow features according to the background difference. Our approach filters and normalizes the descriptors of consecutive frames to establish acceleration feature models. Then, the multifeature models of target templates are initialized by combining acceleration and multiple spatial feature models. Second, we implement data association based on the tracklet confidence by integrating the acceleration and multiple spatial feature affinities. As a result, the optimal associated pairs between target tracklets and detections are solved by the Hungarian algorithm. Finally, our tracking system updates the multifeature models of target templates online depending on the reliability of the tracklets, and the trajectories of multiple targets are output. Experiments conducted on the challenging multiple object tracking benchmark confirm the effectiveness and superiority of the proposed method.
HongBin Liu; Faliang Chang; Chunsheng Liu; Fuxin Liang. Multitarget tracking using multifeature model with acceleration feature. Optical Engineering 2018, 57, 073105 .
AMA StyleHongBin Liu, Faliang Chang, Chunsheng Liu, Fuxin Liang. Multitarget tracking using multifeature model with acceleration feature. Optical Engineering. 2018; 57 (7):073105.
Chicago/Turabian StyleHongBin Liu; Faliang Chang; Chunsheng Liu; Fuxin Liang. 2018. "Multitarget tracking using multifeature model with acceleration feature." Optical Engineering 57, no. 7: 073105.