Box-Grained Reranking Matching for Multi-Camera Multi-Target Tracking

Yang, Xipeng; Ye, Jin; Lu, Jincheng; Gong, Chenting; Jiang, Minyue; Lin, Xiangru; Zhang, Wei; Tan, Xiao; Li, Yingying; Ye, Xiaoqing; Ding, Errui

doi:10.1109/cvprw56347.2022.00349

Cited by 17 publications

(4 citation statements)

References 35 publications

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“…Based on the results obtained from the three modules mentioned above, inter-camera association can be considered as a trajectory matching or trajectory retrieval problem. Many previous works [1][2][3][4][6][7][8] have treated cross-camera matching as a trajectory clustering problem. Recently, many studies have found that traffic rules and spatio-temporal constraints can serve as prior knowledge for filtering candidate trajectories, significantly reducing the search space and thereby greatly improving the accuracy of vehicle ReID.…”

Section: Inter-camera Associationmentioning

confidence: 99%

“…Recently, many studies have found that traffic rules and spatio-temporal constraints can serve as prior knowledge for filtering candidate trajectories, significantly reducing the search space and thereby greatly improving the accuracy of vehicle ReID. Ye et al [2] associated all candidate trajectories between two successive cameras using the Box-Grained Reranking Matching algorithm. Yao et al [3] predefined entry/exit zones and proposed a zone-gate-and time-decay-based matching mechanism to adjust the original appearance matrix.…”

Section: Inter-camera Associationmentioning

confidence: 99%

“…With the rapid development of Intelligent Transportation Systems (ITS) and the increasing demand for digital traffic management platforms, Multi-Camera Multi-Vehicle Tracking (MCMVT) [1][2][3][4][5][6][7][8] has attracted widespread attention in recent years. Applications similar to MCMVT can assist in modern traffic flow prediction and analysis.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Camera Multi-Vehicle Tracking Guided by Highway Overlapping FoVs

Zhang,

Fang,

et al. 2024

Mathematics

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Multi-Camera Multi-Vehicle Tracking (MCMVT) is a critical task in Intelligent Transportation Systems (ITS). Differently to in urban environments, challenges in highway tunnel MCMVT arise from the changing target scales as vehicles traverse the narrow tunnels, intense light exposure within the tunnels, high similarity in vehicle appearances, and overlapping camera fields of view, making highway MCMVT more challenging. This paper presents an MCMVT system tailored for highway tunnel roads incorporating road topology structures and the overlapping camera fields of view. The system integrates a Cascade Multi-Level Multi-Target Tracking strategy (CMLM), a trajectory refinement method (HTCF) based on road topology structures, and a spatio-temporal constraint module (HSTC) considering highway entry–exit flow in overlapping fields of view. The CMLM strategy exploits phased vehicle movements within the camera’s fields of view, addressing such challenges as those presented by fast-moving vehicles and appearance variations in long tunnels. The HTCF method filters static traffic signs in the tunnel, compensating for detector imperfections and mitigating the strong lighting effects caused by the tunnel lighting. The HSTC module incorporates spatio-temporal constraints designed for accurate inter-camera trajectory matching within overlapping fields of view. Experiments on the proposed Highway Surveillance Traffic (HST) dataset and CityFlow dataset validate the system’s effectiveness and robustness, achieving an IDF1 score of 81.20% for the HST dataset.

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Section: Inter-camera Associationmentioning

confidence: 99%

Section: Inter-camera Associationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Camera Multi-Vehicle Tracking Guided by Highway Overlapping FoVs

Zhang,

Fang,

et al. 2024

Mathematics

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“…Multiple-object tracking (MOT), as a high-level task in computer vision, has been widely applied in various fields such as video surveillance [1][2][3], human behavior recognition, and autonomous driving [4][5][6]. One of the advantages of video-based MOT [7][8][9][10][11][12][13][14] is its ability to accurately localize targets continuously in videos while maintaining their identity information, unchanged, when the appearance or the surrounding environment changes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

Li,

Piao

2023

Applied Sciences

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During the Multi-Object Tracking (MOT) process, the effectiveness of Re-Identification (Re-ID) is typically contingent upon the performance of detection. In particular, detection errors may result in the blurring of Re-ID features, thereby diminishing the efficacy of Re-ID. In particular, Re-ID exhibits poor robustness in intricate scenarios such as distance and scale alterations, occlusion, and crowding. Meanwhile, the frequent occurrence of object occlusion can result in the interruption of object tracking, thereby impacting the tracking efficiency. To address these issues, a novel MOT method approach that leverages Re-ID enhancement and association correction is proposed. Through an unanchored, one-stage backbone network with CenterNet as the detector, the object position is detected by predicting the heatmap of the object center point. A constraint on the Predicted Distance Difference Loss (PDDL) is then designed for the response value of the predicted heatmap, effectively enhancing the detection accuracy and reduce the detection bias. To address the issue of Re-ID reliance on detection accuracy and enhance its robustness, an Effective Feature Graph Convolutional Neural network (EFGCN) is proposed. This approach expands learnable effective features by constructing a graph model that enhances them through a GCN model of heatmap true value locations and surrounding response value locations. Additionally, a target trajectory Association Correction Module (ACM) is introduced in the association phase to handle interruptions and trajectory fragments caused by occlusion. By improving data association for tracking judgment when dealing with occluded targets and considering the relationship between trajectory fragmentation and the detection of low-scoring targets, attempts are made to continue with tracking occluded targets to form complete object associations. The proposed method is evaluated on the MOT16 and MOT17 datasets and compared with other algorithms. The results demonstrate that it is highly effective.

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Multi-camera trajectory matching based on hierarchical clustering and constraints

Szűcs,

Borsodi,

Papp

2023

Multimed Tools Appl

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The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise installed fixed position multi-camera configurations, benefit greatly from recent advances. In this paper, we propose a Multi-Camera Multi-Target (MCMT) vehicle tracking system using a constrained hierarchical clustering solution, which improves trajectory matching, and thus provides a more robust tracking of objects transitioning between cameras. YOLOv5, ByteTrack, and ResNet50-IBN ReID networks are used for vehicle detection and tracking. Static attributes such as vehicle type and vehicle color are determined from ReID features with SVM. The proposed ReID feature-based attribute categorization shows better performance, than its pure CNN counterpart. Single-camera trajectories (SCTs) are combined into multi-camera trajectories (MCTs) using hierarchical agglomerative clustering (HAC) with time and space constraints (our proposed algorithm is denoted by MCT#MAC). Similarities between SCTs are measured by comparing the mean ReID features cumulated on the trajectory. The system was evaluated on more datasets, and our experiments demonstrate that constraining HAC by manipulating the proximity matrix greatly improves the multi-camera IDF1 score.

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Box-Grained Reranking Matching for Multi-Camera Multi-Target Tracking

Cited by 17 publications

References 35 publications

Multi-Camera Multi-Vehicle Tracking Guided by Highway Overlapping FoVs

Multi-Camera Multi-Vehicle Tracking Guided by Highway Overlapping FoVs

Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction

Multi-camera trajectory matching based on hierarchical clustering and constraints

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