Cooperative People Tracking by Distributed Cameras Network

Wu, Yi Chang; Chen, Ching-Han; Chiu, Yao Te; Chen, Pi Wei

doi:10.3390/electronics10151780

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Cross-video pedestrian tracking is applied in two application scenarios as follows: overlapping views and nonoverlapping views [28]. In the case of nonoverlapping views, prevailing strategies rely on the similarity of pedestrian features across different surveillance sources [29].…”

Section: Across-video Pedestrian Trackingmentioning

confidence: 99%

Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

Huang,

Li,

Yang

et al. 2024

Sensors

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Pedestrian tracking in surveillance videos is crucial and challenging for precise personnel management. Due to the limited coverage of a single video, the integration of multiple surveillance videos is necessary in practical applications. In the realm of pedestrian management using multiple surveillance videos, continuous pedestrian tracking is quite important. However, prevailing cross-video pedestrian matching methods mainly rely on the appearance features of pedestrians, resulting in low matching accuracy and poor tracking robustness. To address these shortcomings, this paper presents a cross-video pedestrian tracking algorithm, which introduces spatial information. The proposed algorithm introduces the coordinate features of pedestrians in different videos and a linear weighting strategy focusing on the overlapping view of the tracking process. The experimental results show that, compared to traditional methods, the method in this paper improves the success rate of target pedestrian matching and enhances the robustness of continuous pedestrian tracking. This study provides a viable reference for pedestrian tracking and crowd management in video applications.

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Section: Across-video Pedestrian Trackingmentioning

confidence: 99%

Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

Huang,

Li,

Yang

et al. 2024

Sensors

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“…In addition, the distance problem caused by fast motion was reduced by applying optical flow. Wu et al [ 26 ] proposed a three-step cooperative tracking method to track people in a multi-camera environment through tracking token transfer. Zhang et al [ 27 ] proposed an online (real-time) tracking framework, and they improve the cross-camera person recall performance through appearance and spatial–temporal features.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Indoor Multi-Object Tracking in Overlapping FOV Multi-Camera Environments

Jang

Seon

Choi

2022

Sensors

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Multi-Target Multi-Camera Tracking (MTMCT), which aims to track multiple targets within a multi-camera network, has recently attracted considerable attention due to its wide range of applications. The main challenge of MTMCT is to match local tracklets (i.e., sub-trajectories) obtained by different cameras and to combine them into global trajectories across the multi-camera network. This paper addresses the cross-camera tracklet matching problem in scenarios with partially overlapping fields of view (FOVs), such as indoor multi-camera environments. We present a new lightweight matching method for the MTMC task that employs similarity analysis for location features. The proposed approach comprises two steps: (i) extracting the motion information of targets based on a ground projection method and (ii) matching the tracklets using similarity analysis based on the Dynamic Time Warping (DTW) algorithm. We use a Kanade–Lucas–Tomasi (KLT) algorithm-based frame-skipping method to reduce the computational overhead in object detection and to produce a smooth estimate of the target’s local tracklets. To improve matching accuracy, we also investigate three different location features to determine the most appropriate feature for similarity analysis. The effectiveness of the proposed method has been evaluated through real experiments, demonstrating its ability to accurately match local tracklets.

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“…Group activity recognition (GAR) classifies the collective behavior of a group of people in a short video clip of a specific event based on the individual actions of the group members and their interactions with each other [1]. Different from deep learning tasks, such as human activity recognition [2], people tracking [3], and occupancy counting [4], GAR is unique in its potential to explore critical semantic information from interactions among individuals and thus widely used in security surveillance, social role understanding, and sports video analysis.…”

Section: Introductionmentioning

confidence: 99%

Global Individual Interaction Network Based on Consistency for Group Activity Recognition

Huang,

Zhang,

et al. 2023

Electronics

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Modeling the interactions among individuals in a group is essential for group activity recognition (GAR). Various graph neural networks (GNNs) are regarded as popular modeling methods for GAR, as they can characterize the interaction among individuals at a low computational cost. The performance of the current GNN-based modeling methods is affected by two factors. Firstly, their local receptive field in the mapping layer limits their ability to characterize the global interactions among individuals in spatial–temporal dimensions. Secondly, GNN-based GAR methods do not have an efficient mechanism to use global activity consistency and individual action consistency. In this paper, we argue that the global interactions among individuals, as well as the constraints of global activity and individual action consistencies, are critical to group activity recognition. We propose new convolutional operations to capture the interactions among individuals from a global perspective. We use contrastive learning to maximize the global activity consistency and individual action consistency for more efficient recognition. Comprehensive experiments show that our method achieved better GAR performance than the state-of-the-art methods on two popular GAR benchmark datasets.

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Cooperative People Tracking by Distributed Cameras Network

Cited by 6 publications

References 22 publications

Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

Lightweight Indoor Multi-Object Tracking in Overlapping FOV Multi-Camera Environments

Global Individual Interaction Network Based on Consistency for Group Activity Recognition

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