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.

The parking assist system is an essential application of the car’s active collision avoidance system in low-speed and complex urban environments, which has been a hot research topic in recent years. Parking space detection is an important step of the parking assistance system, and its research object is parking spaces with symmetrical structures in parking lots. By analyzing and investigating parking space information measured by the sensors, reliable detection of sufficient parking spaces can be realized. First, this article discusses the main problems in the process of detecting parking spaces, illustrating the research significance and current research status of parking space detection methods. In addition, it further introduces some parking space detection methods, including free-space-based methods, parking-space-marking-based methods, user-interface-based methods, and infrastructure-based methods, which are all under methods of parking space selection. Lastly, this article summarizes the parking space detection methods, which gives a clear direction for future research.

Visual tracking is one of the fundamental problems in computer vision. Recently, some deep-learning-based tracking algorithms have been illustrating record-breaking performances. However, due to the high complexity of neural networks, most deep trackers suffer from low tracking speed, and thus are impractical in many real-world applications. Some recentlyproposed deep trackers with smaller network structure achieve high efficiency while at the cost of significant decrease on precision. In this paper, we propose to transfer the deep feature which is learned originally for image classification to the visual tracking domain. The domain adaptation is achieved via some “grafted” auxiliary networks which are trained by regressing the object location in tracking frames. This adaptation improves the tracking performance significantly, both on accuracy and efficiency. The yielded deep tracker is real-time and also illustrates the state-ofthe-art accuracies in the experiment involving two well-adopted benchmarks with more than 100 test videos. Furthermore, the adaptation is also naturally used for introducing the objectness concept into visual tracking. This removes a long-standing target ambiguity in visual tracking tasks and we illustrate the empirical superiority of the more well-defined task.

Abstract—Human detection is one of the long-standing computer vision tasks, and it has been a cornerstone for many real-world applications such as photo album organization, video surveillance, and autonomous driving. Benefiting from deep learning technologies such as convolutional neural networks, modern object detectors have been achieving much improved accuracy in generic object detection tasks. In this paper, we aim to improve deep learning based human detection. Our main idea is to exploit semantic context information for human detection by using deep-learnt semantic features provided by semantic segmentation masks. Segmentation masks play as an attention mechanism and enforce the detectors to focus on the image regions where potential object candidates are likely to appear. Meanwhile, the extra segmentation mask channel can also guide the convolutional kernels to automatically learn more discriminative features which make it easier to distinguish the background and foreground. We implement our methods with two popular detection frameworks, i.e., Faster R-CNN and SSD, and experimentally analyze the effectiveness of the proposed methods. Evaluation results on the widely used MS-COCO dataset and the very recent CrowdHuman dataset are provided. Our proposed methods outperform the baseline detectors and achieve better performance on highly occluded human detection.

Change detection (CD) in remote sensing imagery is crucial for monitoring environmental variations over time. Recent advancements have introduced numerous effective algorithms, significantly enhancing the performance of CD tasks. However, various challenges still persist, such as complex backgrounds, small targets, and diverse change types. To address these challenges, we propose a novel Mamba-based multi-scale feature enhancement method. Our method leverages the strengths of state space models in handling long sequences by reformulating the pyramid (multi-scale) feature fusion as a sequence processing task. This approach enables our model to efficiently capture both long-range context and fine-grained local details, which are critical for effective feature learning and mitigating the aforementioned challenges. Moreover, we introduce scale-level embedding and auxiliary loss to optimize and regulate the model's training process. These enhancements not only accelerate training but also improve the model's generalization capability. Extensive experiments are conducted on four leading benchmarks: WHUCD, SYSU-CD, LEVIR-CD, and DSIFN-CD datasets. The results demonstrate that our approach consistently delivers state-of-theart performance, surpassing other cutting-edge methods by a significant margin. The source code will be made available at https://github.com/Band-127/Pyramid-Mamba-CD upon formal acceptance of the paper.