Multiple Object Tracking Using Re-Identification Model with Attention Module

Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this...

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Bibliographic Details
Published in:Applied sciences 2023-04, Vol.13 (7), p.4298
Main Authors: Ahn, Woo-Jin, Ko, Koung-Suk, Lim, Myo-Taeg, Pae, Dong-Sung, Kang, Tae-Koo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Classification
Computer vision
Datasets
deep learning
Localization
Machine vision
Methods
multiple object tracking
re-identification
Sensors
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Summary:Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this paper, we propose a triplet-based MOT network that integrates the tracking information and the visual features of the object. Using a triplet-based image feature, the network can differentiate similar-looking objects, reducing the number of identity switches over a long period. Furthermore, an attention-based re-identification model that focuses on the appearance of objects was introduced to extract the feature vectors from the images to effectively associate the objects. The extensive experimental results demonstrated that the proposed method outperforms existing methods on the ID switch metric and improves the detection performance of the tracking system.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13074298