@inproceedings{9b5058ee02914dca96f01669629dc214,
title = "Vehicle re-identification with learned representation and spatial verification and abnormality detection with multi-adaptive vehicle detectors for traffic video analysis",
abstract = "Traffic flow analysis is essential for intelligent transportation systems. In this paper, we propose methods for two challenging problems in traffic flow analysis: vehicle re-identification and abnormal event detection. For the first problem, we propose to combine learned high-level features for vehicle instance representation with hand-crafted local features for spatial verification. For the second problem, we propose to use multiple adaptive vehicle detectors for anomaly proposal and use heuristics properties extracted from anomaly proposals to determine anomaly events. Experiments on the datasets of traffic flow analysis from AI City Challenge 2019 show that our methods achieve mAP of 0.4008 for vehicle re-identification in Track 2, and can detect abnormal events with very high accuracy (F1 = 0.9429) in Track 3.",
author = "Nguyen, {Khac Tuan} and Hoang, {Trung Hieu} and Tran, {Minh Triet} and Le, {Trung Nghia} and Bui, {Ngoc Minh} and Do, {Trong Le} and Vo-Ho, {Viet Khoa} and Luong, {Quoc An} and Tran, {Mai Khiem} and Nguyen, {Thanh An} and Truong, {Thanh Dat} and Nguyen, {Vinh Tiep} and Do, {Minh N.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE Computer Society. All rights reserved.; 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 ; Conference date: 16-06-2019 Through 20-06-2019",
year = "2019",
month = jun,
language = "English (US)",
series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",
publisher = "IEEE Computer Society",
pages = "363--372",
booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019",
}