Multiple Object Tracking Using Re-Identification Model with Attention Module

Author:

Ahn Woo-Jin1ORCID,Ko Koung-Suk1,Lim Myo-Taeg1ORCID,Pae Dong-Sung2ORCID,Kang Tae-Koo3ORCID

Affiliation:

1. School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea

2. Department of Software, Sangmyung University, Cheonan 31066, Republic of Korea

3. Department of Human Intelligence and Robot Engineering, Sangmyung University, Cheonan 31066, Republic of Korea

Abstract

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.

Funder

National Research Foundation of Korea

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference28 articles.

1. Two-stream convolutional networks for action recognition in videos;Simonyan;Adv. Neural Inf. Process. Syst.,2014

2. Deep learning in sport video analysis: A review;Rangasamy;TELKOMNIKA (Telecommun. Comput. Electron. Control),2020

3. Intelligent video surveillance: A review through deep learning techniques for crowd analysis;Sreenu;J. Big Data,2019

4. Multitarget vehicle tracking and motion state estimation using a novel driving environment perception system of intelligent vehicles;Chen;J. Adv. Transp.,2021

5. Multiple object tracking: A literature review;Luo;Artif. Intell.,2021

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