Countor: count without bells and whistles

The effectiveness of an Intelligent transportation system (ITS) relies on the understanding of the vehicles behaviour. Different approaches are proposed to extract the attributes of the vehicles as Re-Identification (ReID) or multi-target single camera tracking (MTSC). The analysis of those attributes leads to the behavioural tasks as multi-target multicamera tracking (MTMC) and Turn-counts (Count vehicles that go through a predefined path). In this work, we propose a novel approach to Turn-counts which uses a MTSC and a proposed path classifier. The proposed method is evaluated on CVPR AI City Challenge 2020. Our algorithm achieves the second place in Turn-counts with a score of 0.9346.

[1]  Stefan Roth,et al.  MOT16: A Benchmark for Multi-Object Tracking , 2016, ArXiv.

[2]  Ming-Hsuan Yang,et al.  UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking , 2015, Comput. Vis. Image Underst..

[3]  Noboru Ohnishi,et al.  A computer vision based vehicle detection and counting system , 2016, 2016 8th International Conference on Knowledge and Smart Technology (KST).

[4]  Ming Tang,et al.  Hierarchical and Networked Vehicle Surveillance in ITS: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.

[5]  Radu Orghidan,et al.  Towards Real Time Vehicle Counting using YOLO-Tiny and Fast Motion Estimation , 2019, 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME).

[6]  Laura Leal-Taixé,et al.  Tracking Without Bells and Whistles , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[7]  Saturnino Maldonado-Bascón,et al.  Extremely Overlapping Vehicle Counting , 2015, IbPRIA.

[8]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[9]  Shiru Qu,et al.  Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition , 2017 .

[10]  Jenq-Neng Hwang,et al.  CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Dietrich Paulus,et al.  Simple online and realtime tracking with a deep association metric , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[12]  Jenq-Neng Hwang,et al.  Single-Camera and Inter-Camera Vehicle Tracking and 3D Speed Estimation Based on Fusion of Visual and Semantic Features , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[13]  Min-Te Sun,et al.  A YOLO-Based Traffic Counting System , 2018, 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

[14]  José M. F. Moura,et al.  FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[15]  Jenq-Neng Hwang,et al.  MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D , 2019, IEEE Access.

[16]  Sergio A. Velastin,et al.  A Review of Computer Vision Techniques for the Analysis of Urban Traffic , 2011, IEEE Transactions on Intelligent Transportation Systems.

[17]  Rosalina Abdul Salam,et al.  Traffic Surveillance : A Review of Vision Based Vehicle Detection , Recognition and Tracking , 2016 .

[18]  Ronald L. Rivest,et al.  Introduction to Algorithms, 3rd Edition , 2009 .

[19]  Rosaldo J. F. Rossetti,et al.  Computer-vision-based surveillance of intelligent transportation systems , 2018, 2018 13th Iberian Conference on Information Systems and Technologies (CISTI).

[20]  Minh N. Do,et al.  STREETS: A Novel Camera Network Dataset for Traffic Flow , 2019, NeurIPS.

[21]  José M. F. Moura,et al.  Understanding Traffic Density from Large-Scale Web Camera Data , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  L. Javier García-Villalba,et al.  Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach , 2019, Sensors.