Multi-Object Tracking (MOT) aims to build moving trajectories of objects within video sequences and serves as a critical component in autonomous driving systems. Recently, several studies have revealed the vulnerability of existing MOT methods by investigating adversarial attacks against MOT, raising significant safety concerns for real-world applications. These methods attack trackers by deliberately inserting false alarms, which mislead trajectories to drift from their correct paths. However, current MOT attack methods fail to propose efficient strategies for generating false alarms, as they either rely on computationally intensive optimization to determine the placement of false alarms, or crudely insert a large number of heuristically designed false alarms. In this paper, we propose an explainable and effective false alarm generation module, named Target Generating Module (TGM), that adaptively determines the location and size of false alarms by leveraging historical trajectory information. Based on this module, we design an attack method targeting mainstream MOT approaches, namedTrajectory-Aware Attack (TA Attack). TA Attack achieves effective disruption of MOT systems by combining detection erasure and false alarm generation, requiring only a few frames to successfully compromise trajectories. To exhibit the flexibility and effectiveness of our method, we conduct experiments using four multi-object trackers (ByteTrack, SORT, CenterTrack and FairMOT) which are enabled by two representative detectors (YOLOX and CenterNet). The results demonstrate our method achieves state of the art performance with 74.87% attack success rate on BDD100K, 81.7% attack success rate on MOT17 and 83.87% attack success rate on MOT20 while 4 frames being attacked averagely, revealing the vulnerability of association mechanism in MOT methods.

Abstract Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hats, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, we use a segmentation network as the foundation and use multiple branches to achieve human segmentation, contour detail extraction, and information fusion. We also propose a foreground probability map module, which uses the feature maps in the segmentation network to pre-estimate the foreground probabilities of each pixel and obtain Semantic Guided Matting Net. Under the condition that only a single image is needed as the input, the human matting task can be realized by making full use of the semantic information in the image. We validate our method on the P3M-10k dataset. Compared with the benchmark, our method has made significant improvements in various evaluation indicators.

Diffusion-based image editing involves both preserving the source image content and generating new content or applying modifications. Although current editing approaches have made improvements under text guidance, they have two key drawbacks: overemphasis on retaining original image info, neglecting editability and text alignment, and inability to handle both structure-consistent and non-rigid editing tasks. In this paper, we propose a zero-shot image editing method, named Enhance Editability for text-based image Editing via Efficient CLIP guidance (E4C), which presents an innovative adaptive feature sharing mechanism to enable multi-task editing. Additionally, a novel random gateway mechanism is designed to efficiently introduce CLIP guidance into the multi-step sampling of diffusion, achieving high congruence between editing results and target text. Comprehensive quantitative and qualitative experiments demonstrate that our method effectively resolves the text alignment issues prevalent in existing methods while maintaining the fidelity to the source image, and performs well across a wide range of editing tasks.

In deep space environment, some objects may split into several small fragments during movement, and these deep space objects often appear as points in satellite images. In this article, we conduct research on multi-object tracking (MOT) for these objects. First, we propose a simulation dataset, ScatterDataset, which simulates the movement and separation of objects in deep space background. By assigning two IDs to a trajectory, we describe the trajectory’s relationship before and after separation. Second, we present an end-to-end motion association model, ScatterNet, which encodes the position information of trajectories and detections into motion features. These features are processed through temporal aggregation by a Transformer encoder and spatial aggregation by a graph network; then, we get the association results by calculating the similarity between these features. Finally, we introduce a tracker, ScatterTracker, which is suitable for tracking in scenarios with object separation. Experiments with state-of-the-art tracking methods on ScatterDataset demonstrate that our approach has achieved significant performance improvements in deep space scenarios. The code is available at:https://github.com/wht-bupt/ScatterTrack.

Mainstream multi-object tracking methods exploit appearance information and/or motion information to achieve interframe association. However, dealing with similar appearance and occlusion is a challenge for appearance information, while motion information is limited by linear assumptions and is prone to failure in nonlinear motion patterns. In this work, we disregard appearance clues and propose a pure motion tracker to address the above issues. It dexterously utilizes Transformer to estimate complex motion and achieves high-performance tracking with low computing resources. Furthermore, contrastive learning is introduced to optimize feature representation for robust association. Specifically, we first exploit the long-range modeling capability of Transformer to mine intention information in temporal motion and decision information in spatial interaction and introduce prior detection to constrain the range of motion estimation. Then, we introduce contrastive learning as an auxiliary task to extract reliable motion features to compute affinity and introduce bidirectional matching to improve the affinity computation distribution. In addition, given that both tasks are dedicated to narrowing the embedding distance between the motion features of the tracked object and the detection features, we design a joint-motion-and-association framework to unify the above two tasks in one framework for optimization. The experimental results achieved with three benchmark datasets, MOT17, MOT20 and DanceTrack, verify the effectiveness of our proposed method. Compared with state-of-the-art methods, the proposed STDFormer sets a new state-of-the-art on DanceTrack and achieves competitive performance on MOT17 and MOT20. This demonstrates the advantage of our method in handling associations under similar appearance, occlusion or nonlinear motion. At the same time, the significant advantages of the proposed method over Transformer-based and contrastive learning-based methods suggest a new direction for the application of Transformer and contrastive learning in MOT. In addition, to verify the generalization of STDFormer in unmanned aerial vehicle (UAV) videos, we also evaluate STDFormer on VisDrone2019. The results show that STDFormer achieves state-of-the-art performance on VisDrone2019, which proves that it can handle small-scale object associations in UAV videos well. The code is available at https://github.com/Xiaotong-Zhu/STDFormer.

Crack is one of the critical factors that degrade the performance of machinery manufacturing equipment. Recently, physics-informed neural networks (PINNs) have received attention due to their strong potential in solving physical problems. For fracture problems, PINNs have been used to predict crack paths by minimizing the variational energy of discrete domains where refined meshes are necessary. To obtain refined meshes, posteriori adaptive refinement techniques are commonly used to perform local refinement of the mesh based on errors in the intermediate calculation process; thus, they require pretest calculations. However, it is computationally expensive to precalculate complex problems, especially crack propagation. To solve this problem, we propose a PointNet-based adaptive refinement method to avoid precalculation when constructing the discrete domain. The proposed method is applied to simulate crack propagation using a PINN. Results show that the proposed method can be used to obtain reliable results efficiently when using the PINN framework.

In object detection, offset-guided and point-guided regression dominate anchor-based and anchor-free method separately. Recently, point-guided approach is introduced to anchor-based method. However, we observe points predicted by this way are misaligned with matched region of proposals and score of localization, causing a notable gap in performance. In this paper, we propose CPM R-CNN which contains three efficient modules to optimize anchor- based point-guided method. According to sufficient evaluations on the COCO dataset, CPM R-CNN is demonstrated efficient to improve the localization accuracy by calibrating mentioned misalignment. Compared with Faster R-CNN and Grid R-CNN based on ResNet-101 with FPN, our approach can substantially improve detection mAP by 3.3% and 1.5% respectively without whistles and bells. Moreover, our best model achieves improvement by a large margin to 49.9% on COCO test-dev. Code is available at https://github.com/zhubinQAQ/CPM-R-CNN.

Summary Taxi GPS trajectories can be mined and used to optimize urban traffic scheduling. The optimization of traffic scheduling is important, especially in megacities such as Beijing. In this paper, we analyze the traffic features in Beijing by mining taxi GPS trajectories. We define the Congestion Coefficient of each edge of the taxi trajectory as the consumed time of a taxi running over a unit of distance. By analyzing the distribution of congestion coefficients of all taxi trajectories, we can observe that, on working days, (1) the congestion coefficient is between 0 and 2 (average speed is greater than 0.5 m/s) and is acceptable to taxi drivers, (2) the morning rush hours are 7:00 ∼ 10:00, (3) the evening rush hours are 17:00 ∼ 20:00, and (4) the traffic congestion in the morning rush hours is worse than that in the evening rush hours; on the weekend, (1) the congestion coefficient is less than 0.2 (average speed is greater than 5 m/s) and is acceptable to taxi drivers; (2) compared with the traffic congestion on working days, there are no significant morning rush hours on the weekend; and (3) the period of the time between 13:00 and 15:00 could be considered the traffic rush hours on the weekend. These findings can be used to improve urban traffic management.

Detecting human parts at instance-level is an essential prerequisite for the analysis of human keypoints, actions, and attributes. Nonetheless, there is a lack of a large-scale, rich-annotated dataset for human parts detection. We fill in the gap by proposing COCO Human Parts. The proposed dataset is based on the COCO 2017, which is the first instance-level human parts dataset, and contains images of complex scenes and high diversity. For reflecting the diversity of human body in natural scenes, we annotate human parts with (a) location in terms of a bounding-box, (b) various type including face, head, hand, and foot, (c) subordinate relationship between person and human parts, (d) fine-grained classification into right-hand/left-hand and left-foot/right-foot. A lot of higher-level applications and studies can be founded upon COCO Human Parts, such as gesture recognition, face/hand keypoint detection, visual actions, human-object interactions, and virtual reality. There are a total of 268,030 person instances from the 66,808 images, and 2.83 parts per person instance. We provide a statistical analysis of the accuracy of our annotations. In addition, we propose a strong baseline for detecting human parts at instance-level over this dataset in an end-to-end manner, call Hier(archy) R-CNN. It is a simple but effective extension of Mask R-CNN, which can detect human parts of each person instance and predict the subordinate relationship between them. Codes and dataset are publicly available (https://github.com/soeaver/Hier-R-CNN).