Collaborative perception in multi-agent system enhances overall perceptual capabilities by facilitating the exchange of complementary information among agents. Current mainstream collaborative perception methods rely on discretized feature maps to conduct fusion, which however, lacks flexibility in extracting and transmitting the informative features and can hardly focus on the informative features during fusion. To address these problems, this paper proposes a novel Anchor-Centric paradigm for Collaborative Object detection (ACCO). It avoids grid precision issues and allows more flexible and efficient anchor-centric communication and fusion. ACCO is composed by three main components: (1) Anchor featuring block (AFB) that targets to generate anchor proposals and projects prepared anchor queries to image features. (2) Anchor confidence generator (ACG) is designed to minimize communication by selecting only the features in the confident anchors to transmit. (3) A local-global fusion module, in which local fusion is anchor alignment-based fusion (LAAF) and global fusion is conducted by spatial-aware cross-attention (SACA). LAAF and SACA run in multilayers, so agents conduct anchor-centric fusion iteratively to adjust the anchor proposals. Comprehensive experiments are conducted to evaluate ACCO on OPV2V and Dair-V2x datasets, which demonstrate ACCO's superiority in reducing the communication volume, and in improving the perception range and detection performances. Code can be found at: https://github.com/sidiangongyuan/ACCO.

Existing tracking-by-detection Multi-Object Tracking methods mainly rely on associating objects with tracklets using motion and appearance features. However, variations in viewpoint and occlusions can result in discrepancies between the features of current objects and those of historical tracklets. To tackle these challenges, this paper proposes a novel Spatial-Temporal Tracklet Graph Matching paradigm (STAR). The core idea of STAR is to achieve long-term, reliable object association through the association of ``tracklet clips (TCs)". TCs are segments of confidently associated multi-object trajectories, which are linked through graph matching. Specifically, STAR initializes TCs using a Confident Initial Tracklet Generator (CITG) and constructs a TC graph via Tracklet Clip Graph Construction (TCGC). In TCGC, each object in a TC is treated as a vertex, with the appearance and local topology features encoded on the vertex. The vertices and edges of the TC graph are then updated through message propagation to capture higher-order features. Finally, a Tracklet Clip Graph Matching (TCGM) method is proposed to efficiently and accurately associate the TCs through graph matching. STAR is model-agnostic, allowing for seamless integration with existing methods to enhance their performance. Extensive experiments on diverse datasets, including MOTChallenge, DanceTrack, and VisDrone2021-MOT, demonstrate the robustness and versatility of STAR, significantly improving tracking performance under challenging conditions.

Current deep learning-based methods for remote sensing image dehazing have developed rapidly, yet they still commonly struggle to simultaneously preserve fine texture details and restore accurate colors. The fundamental reason lies in the insufficient modeling of high-frequency information that captures structural details, as well as the lack of effective constraints for color restoration. To address the insufficient modeling of global high-frequency information, we first develop an omni-directional high-frequency feature in painting mechanism that leverages the wavelet transform to extract multi-directional high-frequency components. While maintaining the advantage of linear complexity, it models global long-range texture dependencies through cross-frequency perception. Then, to further strengthen local high-frequency representation, we design a high-frequency prompt attention module that dynamically injects wavelet-domain optimized high-frequency features as cross-level guidance signals, significantly enhancing the model's capability in edge sharpness restoration and texture detail reconstruction. Further, to alleviate the problem of inaccurate color restoration, we propose a color contrast loss function based on the HSV color space, which explicitly models the statistical distribution differences of brightness and saturation in hazy regions, guiding the model to generate dehazed images with consistent colors and natural visual appearance. Finally, extensive experiments on multiple benchmark datasets demonstrate that the proposed method outperforms existing approaches in both texture detail restoration and color consistency. Further results and code are available at: https://github.com/fyxnl/C4RSD.

This article proposes a general solution for point set matching that effectively addresses the challenges of low-overlap, sparse, or featureless point set matching (LSFPM). Unlike previous methods that mainly rely on feature or neighborhood similarity that often fail under such difficult conditions, this work proposes a Geometry-based Point Matching (GPM) method. GPM first introduces two geometric concepts: the “Structural Element” (SE) and the “Superstructural Element” (SSE), both of which are constructed based on local geometric structures. The SSE is an enhanced version of the SE. A descriptor for the SE, called the SE Descriptor (SED), is designed to encode the SE and facilitate an efficient geometry-based similarity metric. We demonstrate that the cosine similarity of SEDs is invariant to scale and rotation. Subsequently, a SE Matching Maximization (SEMM) problem is formulated to identify a size-penalized SSE set that maximizes the sum of similarities. This problem is efficiently solved using the proposed SEMM-MCMC (Markov Chain Monte Carlo) algorithm. The matched SSEs then vote on corresponding point matches, generating high-confidence one-to-one matches, low-confidence one-to-one matches and one-to-many matches. Finally, the InferMatch algorithm is proposed to jointly assess low-confidence one-to-one point set matching while simultaneously distinguishing one-to-many point set matching. The GPM approach can also complement other feature-based and motion-based methods. It has been extensively validated on both synthetic and real datasets, demonstrating its versatility in addressing various point set matching problems, and is not limited to the LSFPM problem. Extensive experiments on diverse datasets, including SPair-71k, UAVDT, VisDrone2021-MOT, and SparseMatch, further demonstrate the robustness and versatility of GPM. The proposed approach significantly improves matching performance under challenging conditions and effectively addresses key limitations of existing point set matching methods.

Nowadays, industrial redundant manipulators have been playing important roles in manufacturing fields, such as welding and assembling, by performing repetitive and dull work. Such long-term industrial operations usually require redundant manipulators to keep good working conditions and maintain steadiness of joint actuation. However, some joints of redundant manipulators may fall into fault status after enduring long-period heavy manipulations, causing that the desired industrial tasks cannot be accomplished accurately. In this article, we propose a novel fault-tolerant method with simultaneous fault-diagnose function for motion planning and control of industrial redundant manipulators. The proposed approach is able to adaptively localize which joints run away from the normal state to be fault, and it can guarantee to finish the desired path tracking control even when these fault joints lose their velocity to actuate. Simulation and experiment results on a Kuka LBR iiwa manipulator demonstrate the efficiency of the proposed fault-tolerant method for motion control of the redundant manipulator.

The crowd stampede and terrorist attacks in public areas have now become more serious and dangerous threats due to the rapid increase in the population and scale of cities. Therefore, the analysis of crowd aggregation behavior has been a new research focus in the field of intelligent video surveillance. However, such public area scenes not only contain moving crowd but also contain other types of objects. The sizes of these objects are usually small, which make their appearances quite similar. Moreover, the individuals in a crowd move randomly and often occlude each other. All the above factors make the analysis of crowd aggregation very difficult. In this paper, the authors attempt to solve this problem in three aspects. First, a novel global feature is used to represent the moving crowd. This feature can well describe the spatial and the temporal motion information of points-of-interest. Second, a strategy is adopted to cluster the feature points first and then calculate the collectiveness. This makes the collectiveness computation of individual groups more consistent and effective. Finally, more comprehensive collective crowd descriptors are proposed to provide a detailed description of the crowd status. Based on the proposed descriptor, the authors realize the evolution analysis of the group movement and the crowd abnormal detection. The experiment results show that the proposed method is able to efficiently compute the crowd collectiveness in various public areas and provide a reliable reference for the public safety management.