This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target’s posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring.

The increasing popularity of vehicles has led to traffic congestion and frequent traffic accidents. Intelligent transportation technology is an effective solution to this problem. In order to improve the accuracy and effectiveness of vehicle detection and tracking, this paper combined the improved YOLOv5s model with the optimized DeepSORT tracking algorithm to detect and track vehicles on traffic roads. Firstly, in the detection model of YOLOv5s, the Attention-based Intra-scale Feature Interaction (AIFI) module is introduced to detect vehicles more quickly and accurately. Secondly, the Kalman filtering (KF) algorithm of DeepSORT is optimized to improve the accuracy of predictions of the vehicle state by using the width to replace the length-to-width ratio of the vehicle prediction box in the original KF algorithm. Finally, in the re-recognition network of DeepSORT, the original Convolutional Neural Network (CNN) model is replaced by an improved ResNet36 as the backbone network for feature extraction. The experimental results show that, compared with the original algorithm, when examining the performance of the improved algorithm in terms of target detection, the recall rate, average accuracy (mAP), and detection speed, are increased by 7.7%, 15.5%, and 14.2%, respectively; in terms of multi-object tracking performance, such as multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA), improvements of 14.84% and 9.62%, respectively, are obtained and the total number of times a trajectory is fragmented (Frag) is reduced by 32.52%.These results indicate that the proposed algorithm can meet the requirements of accuracy, real-time detection, and stable vehicle detection and tracking on traffic roads.

High-efficiency video coding (HEVC) outperforms H.264 in coding efficiency. However, the rate–distortion optimization (RDO) process in coding tree unit (CTU) partitioning requires an exhaustive exploration of all possible quad-tree partitions, resulting in high encoding complexity. To simplify this process, this paper proposed a convolution neural network (CNN) based optimization algorithm combined with a hybrid attention mechanism module. Firstly, we designed a CNN compatible with the current coding unit (CU) size to accurately predict the CU partitions. In addition, we also designed a convolution block to enhance the information interaction between CU blocks. Then, we introduced the convolution block attention module (CBAM) into CNN, called CNN-CBAM. This module concentrates on important regions in the image and attends to the target object correctly. Finally, we integrated the CNN-CBAM into the HEVC coding framework for CU partition prediction in advance. The proposed network was trained, validated, and tested using a large scale dataset covering various scenes and objects, which provides extensive samples for intra-frame CU partition prediction in HEVC. The experimental findings demonstrate that our scheme can reduce the coding time by up to 64.05% on average compared to a traditional HM16.5 encoder, with only 0.09 dB degradation in BD-PSNR and a 1.94% increase in BD-BR.

Among a series of video coding standards jointly developed by ITU-T, VCEG, and MPEG, high-efficiency video coding (HEVC) is one of the most widely used video coding standards today. Therefore, it is still necessary to further reduce the coding complexity of HEVC. In the HEVC standard, a flexible partitioning procedure entitled “quad-tree partition” is proposed to significantly improve the coding efficiency, which, however, leads to high coding complexity. To reduce the coding complexity of the intra-frame prediction, this paper proposes a scheme based on a densely connected convolution neural network (D-CNN) to predict the partition of coding units (CUs). Firstly, a densely connected block was designed to improve the efficiency of the CU partition by fully extracting the pixel features of CTU. Then, efficient channel attention (ECA) and adaptive convolution kernel size were applied to a fast CU partition for the first time to capture the information of the D-CNN convolution channels. Finally, a threshold optimization strategy was formulated to select the best threshold for each depth to further balance the computation complexity of video coding and the performance of RD. The experimental results show that the proposed method reduces the encoding time of HEVC by 60.14%, with a negligible reduction in RD performance, which is better than the existing fast partitioning methods.

High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree coding unit (CU) partition structure to improve video compression efficiency. The determination of the optimal CU partition is achieved through the brute-force search rate-distortion optimization method, which may result in high encoding complexity and hardware implementation challenges. To address this problem, this paper proposes a method that combines convolutional neural networks (CNN) with joint texture recognition to reduce encoding complexity. First, a classification decision method based on the global and local texture features of the CU is proposed, efficiently dividing the CU into smooth and complex texture regions. Second, for the CUs in smooth texture regions, the partition is determined by terminating early. For the CUs in complex texture regions, a proposed CNN is used for predictive partitioning, thus avoiding the traditional recursive approach. Finally, combined with texture classification, the proposed CNN achieves a good balance between the coding complexity and the coding performance. The experimental results demonstrate that the proposed algorithm reduces computational complexity by 61.23%, while only increasing BD-BR by 1.86% and decreasing BD-PSNR by just 0.09 dB.

Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed. Firstly, a constraint mechanism is introduced that uses labels to control the backbone of the network during training to enhance the consistency of the unchanged regions and the differences between the changed regions in the extracted dual-temporal images. Secondly, a Cross-Spatial-Channel Attention (CSCA) module is proposed, which realizes the interaction of valid information between dual-temporal feature maps through channels and spatial attention and uses multi-level information for more accurate detection. The verification results show that compared with advanced parallel methods, CINet achieved the highest F1 scores on all six widely used remote sensing benchmark datasets, reaching a maximum of 92.00 (on LEVIR-CD dataset). These results highlight the excellent ability of CINet to detect changes in various practical scenarios, demonstrating the effectiveness and feasibility of the proposed constraint enhancement and CSCA module.