iTASK - Intelligent Traffic Analysis Software Kit

• Main Authors: Tran, Minh-Triet, Nguyen, Tam V., Hoang, Trung-Hieu, Le, Trung-Nghia, Nguyen, Khac-Tuan, Dinh, Dat-Thanh, Nguyen, Thanh-An, Nguyen, Hai-Dang, Hoang, Xuan-Nhat, Nguyen, Trong-Tung, Vo-Ho, Viet-Khoa, Do, Trong-Le, Nguyen, Lam, Le, Minh-Quan, Nguyen-Dinh, Hoang-Phuc, Pham, Trong-Thang, Nguyen, Xuan-Vy, Nguyen, E-Ro, Tran, Quoc-Cuong, Tran, Hung, Dao, Hieu, Tran, Mai-Khiem, Nguyen, Quang-Thuc, Nguyen, Tien-Phat, Vu-Le, The-Anh, Diep, Gia-Han, Do, Minh N.
• Format: Conference Proceeding
• Language: English
• Subjects: Anomaly detection; Artificial intelligence; Detectors; Event detection; Task analysis; Tracking; Urban areas
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW50498.2020.00314

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