In this work, by re-examining the “matching” nature of Anomaly Detection (AD), we propose a novel AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed. In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion. Given a test sample, the top- K most similar training images are first selected based on a robust histogram matching process. Secondly, the nearest neighbor of each test patch is retrieved over the similar geometrical locations on those “most similar images”, by using a carefully trained local metric. Finally, the anomaly score of each test image patch is calculated based on the distance to its “nearest neighbor” and the “non-background” probability. The proposed method is termed “Cascade Patch Retrieval” (CPR) in this work. Different from the previous patch-matching-based AD algorithms, CPR selects proper “targets” (reference images and patches) before “shooting” (patch-matching). On the well-acknowledged MVTec AD, BTAD and MVTec-3D AD datasets, the proposed algorithm consistently outperforms all the comparing SOTA methods by remarkable margins, measured by various AD metrics. Furthermore, CPR is extremely efficient. It runs at the speed of 113 FPS with the standard setting while its simplified version only requires less than 1 ms to process an image at the cost of a trivial accuracy drop. The code of CPR is available at https://github.com/flyinghu123/CPR.

Currently, learning-based multi-view stereo (MVS) has been dominated by the pipeline of 3D cost volume and regularization network over the static cost volume for depth regression. However, this methodology is plagued by heavy time and memory consumption, which greatly hinders the applications of these methods for real-world high-resolution images. To address these challenges, we present Effi-MVS+, an efficient multi-scale dynamic cost volume based MVS method. Firstly, instead of constructing a static cost volume and predicting a probability distribution map for depth regression, we update the depth map by iteratively predicting depth residuals. In each iteration, we construct a lightweight dynamic cost volume by encoding local matching and regularization information. The dynamic cost volume is subsequently processed using a 2D convolution-based GRU, which owns significant advantages in computational complexity and efficiency. Secondly, we propose a cross-scale propagation mechanism to enhance the multi-scale dynamic cost volume. This mechanism facilitates the progressive aggregation of multi-scale information, thereby providing enhanced matching and regularization information. Thirdly, to further improve the efficiency, we provide a reliable initial depth map to launch the framework and guarantee fast convergence. Extensive experiments on the DTU and Tanks & Temples benchmarks demonstrate the superiority of our method, which outperforms other state-of-the-art methods by a large margin in terms of reconstruction quality, speed, and memory usage . Code will be released at https://github.com/npucvr/Effi-MVS-plus.

LiDAR depth-only completion is a challenging task to estimate dense depth maps only from sparse measurement points obtained by LiDAR. Even though the depth-only methods have been widely developed, there is still a significant performance gap with the RGB-guided methods that utilize extra color images. We find that existing depth-only methods can obtain satisfactory results in the areas where the measurement points are almost accurate and evenly distributed (denoted as normal areas), while the performance is limited in the areas where the foreground and background points are overlapped due to occlusion (denoted as overlap areas) and the areas where there are no measurement points around (denoted as blank areas) since the methods have no reliable input information in these areas. Building upon these observations, we propose an effective Coupled U-Net (CU-Net) architecture for depth-only completion. Instead of directly using a large network for regression, we employ the local U-Net to estimate accurate values in the normal areas and provide the global U-Net with reliable initial values in the overlap and blank areas. The depth maps predicted by the two coupled U-Nets are fused by learned confidence maps to obtain final results. In addition, we propose a confidence-based outlier removal module, which removes outliers using simple judgment conditions. Our proposed method boosts the final results with fewer parameters and achieves state-of-the-art results on the KITTI benchmark. Moreover, it owns a powerful generalization ability under various depth densities, varying lighting, and weather conditions.

Camera vision calibration is the key technology of Video-based Traffic Monitoring System (VTMS). PTZ camera is used more and more widely in the VTMS for its high flexibility and mobility which also bring great difficulties in its calibration. This paper presented an automatic method of calibration for PTZ camera used in Expressway Monitoring System so that the system can implement calibration without any manual intervention as soon as the operator changes the situation of the camera. A novel model of traffic monitoring camera is given in the beginning, a theoretical framework is established, aimed at computing all the unknown parameters in the model. Test results using real data are described. Both accuracy and speed are reported. Implementation ways and steps of practical application in the project are given.

Internet of Things (IoT)-based intelligent transportation is attracting more and more attention. As a key component of intelligent transportation, traffic video monitoring is very important, in which vehicle and pedestrian detection on the road is a crucial task. Although vehicle and pedestrian detection through deep learning (DL) may achieve high accuracy, it tends to require high computing resources, which hinders its use on IoT devices. As an important class of IoT devices, digital signal processor (DSP) has the characteristics of low energy consumption, small size, and strong performance, which has been widely used in intelligent transportation. In order to use DL on DSP for accurate vehicle and pedestrian detection, we first propose a series of general tactics to optimize the object detection convolutional neural network (CNN) model, including convolution layer optimization, cache optimization, compiler optimization, intrinsics optimization and direct memory access (DMA) acceleration, and then a parallel scheme to extend the model to run on multicore, and further quantize the implementation of the model. We evaluate it on UA-DETRAC and KITTI datasets. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with only 0.06% accuracy loss.