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iTASK - Intelligent Traffic Analysis Software Kit

Authors :
Hai-Dang Nguyen
Dat-Thanh Dinh
Mai-Khiem Tran
Gia-Han Diep
Lam Thanh Nguyen
Minh-Quan Le
Trong-Tung Nguyen
Hung Tran
Xuan-Nhat Hoang
Hieu Dao
Tien-Phat Nguyen
E-Ro Nguyen
Khac-Tuan Nguyen
The-Anh Vu-Le
Minh N. Do
Thanh-An Nguyen
Trong-Le Do
Trong-Thang Pham
Viet-Khoa Vo-Ho
Trung-Hieu Hoang
Quoc-Cuong Tran
Xuan-Vy Nguyen
Minh-Triet Tran
Tam V. Nguyen
Quang-Thuc Nguyen
Trung-Nghia Le
Hoang-Phuc Nguyen-Dinh
Source :
CVPR Workshops
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

Traffic flow analysis is essential for intelligent transportation systems. In this paper, we introduce our Intelligent Traffic Analysis Software Kit (iTASK) to tackle three challenging problems: vehicle flow counting, vehicle re-identification, and abnormal event detection. For the first problem, we propose to real-time track vehicles moving along the desired direction in corresponding motion-of-interests (MOIs). For the second problem, we consider each vehicle as a document with multiple semantic words (i.e., vehicle attributes) and transform the given problem to classical document retrieval. For the last problem, we propose to forward and backward refine anomaly detection using GAN-based future prediction and backward tracking completely stalled vehicle or sudden-change direction, respectively. Experiments on the datasets of traffic flow analysis from AI City Challenge 2020 show our competitive results, namely, S1 score of 0.8297 for vehicle flow counting in Track 1, mAP score of 0.3882 for vehicle re-identification in Track 2, and S4 score of 0.9059 for anomaly detection in Track 4. All data and source code are publicly available on our project page. 1

Details

Database :
OpenAIRE
Journal :
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Accession number :
edsair.doi...........4f676368c0de345645731c78c3bb992a
Full Text :
https://doi.org/10.1109/cvprw50498.2020.00314