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Regression-Aware Classification Feature for Pedestrian Detection and Tracking in Video Surveillance Systems

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Abstract
Pedestrian detection and tracking in video surveillance systems is a complex task in computer vision research, which has widely used in many applications such as abnormal action detection, human pose, crowded scenes, fall detection in elderly humans, social distancing detection in the Covid-19 pandemic. This task is categorized into two sub-tasks: detection, and re-identification task. Previous methods independently treat two sub-tasks, only focusing on the re-identification task without employing re-detection. Since the performance of pedestrian detection directly affects the results of tracking, leveraging the detection task is crucial for improving the re-identification task. The total inference time is computed in both the detection and re-identification process, quite far from real-time speed. This paper joins both sub-tasks in a single end-to-end network based on Convolutional Neural Networks (CNNs). Moreover, the detection includes the classification and regression task. As both tasks have a positive correlation, separately learning classification and regression hurts the overall performance. Hence, this work introduces the Regression-Aware Classification Feature (RACF) module to improve feature representation. The convolutional layer is the core component of CNNs, which extracts local features without modeling global features. Therefore, the Cross-Global Context (CGC) is proposed to form long-range dependencies for learning appearance embedding of re-identification features. The proposed model is conducted on the challenging benchmark datasets, MOT17, which surpasses the state-of-the-art online trackers.
Author(s)
보 수언 투이쩐 띠엔 닷웬 주이 린조강현
Issued Date
2021
Type
Article
Keyword
Convolution Neural Networks (CNNs)Pedestrian detectionTracking and Re-identificationVideo surveillance system
DOI
10.1007/978-3-030-84522-3_66
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9160
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_3_030_84522_3_66&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Regression-Aware%20Classification%20Feature%20for%20Pedestrian%20Detection%20and%20Tracking%20in%20Video%20Surveillance%20Systems&offset=0&pcAvailability=true
Publisher
Lecture Notes in Computer Science
Location
스위스
Language
영어
ISSN
0302-9743
Citation Volume
12836
Citation Number
1
Citation Start Page
816
Citation End Page
828
Appears in Collections:
Engineering > IT Convergence
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