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

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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jiayue Li [verfasserIn] Yan Piao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	multiple-object tracking association correction distance difference loss graph convolutional neural network

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 17, p 9528
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:17, p 9528

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app13179528

Katalog-ID:	DOAJ093532547

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callnumber-first	T - Technology
author	Jiayue Li
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title	Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction
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title_sort	multi-object tracking based on re-identification enhancement and associated correction
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title_auth	Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction
url	https://doi.org/10.3390/app13179528 https://doaj.org/article/7816442c1b844cdfa1df6b8f17eb6dd1 https://www.mdpi.com/2076-3417/13/17/9528 https://doaj.org/toc/2076-3417
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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. 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