Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-Adaptive Vehicle Detectors for Traffic Video Analysis

Khac-Tuan Nguyen, Trung-Hieu Hoang, Minh-Triet Tran, Trung-Nghia Le, Ngoc-Minh Bui, Trong-Le Do, Viet-Khoa Vo-Ho, Quoc-An Luong, Mai-Khiem Tran, Thanh-An Nguyen, Thanh-Dat Truong, Vinh-Tiep Nguyen, Minh N. Do; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 363-372

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.

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[bibtex]
@InProceedings{Nguyen_2019_CVPR_Workshops,
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.},
title = {Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-Adaptive Vehicle Detectors for Traffic Video Analysis},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}