Multiple pedestrian tracking by combining particle filter and network flow model

Cui, Yuxin; Zhang, Jingru; He, Zhenyu; Hu, Jianjun

doi:10.1016/j.neucom.2019.03.077

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…Zhao et al explored fundamental concepts, solution algorithms, and application guidance associated with using infrastructure-based LiDAR sensors to accurately detect and track pedestrians and vehicles at intersections [8]. Cui et al proposed a two-stage network flow model for multiple pedestrian tracking [9]. A tracking-by-detection framework is used in the local stage to generate confident tracklets with boosted particle filter.…”

Section: Related Workmentioning

confidence: 99%

Tracking Pedestrians Under Occlusion in Parking Space

Zhou¹,

Yamada²,

Watanabe³

et al. 2023

Computer Systems Science and Engineering

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Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians' sudden appearance in the vehicle's path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision digital map of the parking lot, pedestrians' smart device's sensing data, and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot. However, this subject has not been studied and explored in existing studies. To fill this void, this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces. We also evaluate the proposed method through real-world experiments. The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy. It can also be used for pedestrian tracking in parking spaces.

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Section: Related Workmentioning

confidence: 99%

Tracking Pedestrians Under Occlusion in Parking Space

Zhou¹,

Yamada²,

Watanabe³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The model is then applied to pattern match the entire image and locate the most similar region. Typical generative model tracking algorithms include tracking algorithms based on Kalman filter [25], particle filter [26,27] and mean shift [28]. The generative methods only focus on tracking the target itself but ignore the background information, which is prone to tracking drift when the target is occluded or deforms drastically.…”

Section: Related Work 21 Occlusion and Deformation Handling In Visual...mentioning

confidence: 99%

Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

et al. 2022

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Although the performance of unmanned aerial vehicle (UAV) tracking has benefited from the successful application of discriminative correlation filters (DCF) and convolutional neural networks (CNNs), UAV tracking under occlusion and deformation remains a challenge. The main dilemma is that challenging scenes, such as occlusion or deformation, are very complex and changeable, making it difficult to obtain training data covering all situations, resulting in trained networks that may be confused by new contexts that differ from historical information. Data-driven strategies are the main direction of current solutions, but gathering large-scale datasets with object instances under various occlusion and deformation conditions is difficult and lacks diversity. This paper proposes an attention-based mask generation network (AMGN) for UAV-specific tracking, which combines the attention mechanism and adversarial learning to improve the tracker’s ability to handle occlusion and deformation. After the base CNN extracts the deep features of the candidate region, a series of masks are determined by the spatial attention module and sent to the generator, and the generator discards some features according to these masks to simulate the occlusion and deformation of the object, producing more hard positive samples. The discriminator seeks to distinguish these hard positive samples while guiding mask generation. Such adversarial learning can effectively complement occluded and deformable positive samples in the feature space, allowing to capture more robust features to distinguish objects from backgrounds. Comparative experiments show that our AMGN-based tracker achieves the highest area under curve (AUC) of 0.490 and 0.349, and the highest precision scores of 0.742 and 0.662, on the UAV123 tracking benchmark with partial and full occlusion attributes, respectively. It also achieves the highest AUC of 0.555 and the highest precision score of 0.797 on the DTB70 tracking benchmark with the deformation attribute. On the UAVDT tracking benchmark with the large occlusion attribute, it achieves the highest AUC of 0.407 and the highest precision score of 0.582.

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“…Face tracking is the process of predicting the motion information in subsequent frames based on the motion information in the initial frame of a face to determine the face trajectory and its morphological changes. Traditional face tracking mainly matches for a single feature, extracting a single colour feature, edge feature, texture feature or motion information, [1][2][3] with low robustness. With the rapid development of the IoT in the 21st century, target detection and target tracking 4 are widely used in the IoT and artificial intelligence technologies, 5 intelligent surveillance, Intel-Link traffic, human-machine interfaces, and so forth, and play a crucial role in electronic intelligence, security, military and other fields.…”

Section: Introductionmentioning

confidence: 99%

Multiple feature fusion‐based video face tracking for IoT big data

Liu

Huo

et al. 2021

Int J of Intelligent Sys

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With the advancement of Internet of Things (IoT) and artificial intelligence technologies, and the need for rapid application growth in fields, such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. However, in the process of acquiring facial feature information, face information is easily affected by factors, such as object occlusion, lighting changes, and similar backgrounds. In this paper, we propose a multifeature fusion algorithm based on integral histograms and a real‐time update tracking particle filtering (PF) module. First, edge features and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour features and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the PF algorithm to simplify the calculation steps of complex particles and improve operational efficiency. Finally, the tracking window size is adjusted in real‐time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti‐interference ability and robustness compared with extracting a single feature.

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Multiple pedestrian tracking by combining particle filter and network flow model

Cited by 14 publications

References 19 publications

Tracking Pedestrians Under Occlusion in Parking Space

Tracking Pedestrians Under Occlusion in Parking Space

Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

Multiple feature fusion‐based video face tracking for IoT big data

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