Robust and Online Vehicle Counting at Crowded Intersections

In this paper, we propose an online movement-specific vehicle counting system to realize robust traffic flow analysis at crowded intersections. Our proposed framework adopts PP-YOLO as the vehicle detector and adapts the Deep-Sort algorithm to perform multi-object tracking. In order to realize online and robust vehicle counting, we further adopt a shape-based movement assignment strategy to differentiate movements and carefully designed spatial constraints to effectively reduce false-positive counts. Our proposed framework achieves the overall S1-score of 0.9467, ranking the first in the AICITY2021-track1 challenge.

[1]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[2]  Fabio Tozeto Ramos,et al.  Simple online and realtime tracking , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[3]  Yu Liu,et al.  POI: Multiple Object Tracking with High Performance Detection and Appearance Feature , 2016, ECCV Workshops.

[4]  Jason Yosinski,et al.  An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution , 2018, NeurIPS.

[5]  Fernando Boto,et al.  Real-Time People Counting Using Multiple Lines , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.

[6]  Wei Zhang,et al.  Robust Movement-Specific Vehicle Counting at Crowded Intersections , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  K. V. Embleton,et al.  Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method , 2003 .

[8]  Huansheng Song,et al.  Vehicle Counting System using Deep Learning and Multi-Object Tracking Methods , 2020, Transportation Research Record: Journal of the Transportation Research Board.

[9]  Jenq-Neng Hwang,et al.  Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models , 2019, CVPR Workshops.

[10]  Oihana Otaegui,et al.  Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification , 2012, IEEE Transactions on Intelligent Transportation Systems.

[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]  Xin Zhong,et al.  Automated Counting and Tracking of Vehicles , 2017, Int. J. Pattern Recognit. Artif. Intell..

[13]  Jing Lv,et al.  Vehicle counting in crowded scenes with multi-channel and multi-task convolutional neural networks , 2017, J. Vis. Commun. Image Represent..

[14]  Hui Shen,et al.  PP-YOLO: An Effective and Efficient Implementation of Object Detector , 2020, ArXiv.

[15]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Tao Kong,et al.  SOLOv2: Dynamic, Faster and Stronger , 2020, ArXiv.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[20]  Bastian Goldlücke,et al.  Variational Analysis , 2014, Computer Vision, A Reference Guide.

[21]  Mohamed Adnane Mahraz,et al.  Vehicle counting system in real-time , 2018, 2018 International Conference on Intelligent Systems and Computer Vision (ISCV).

[22]  Xu Yun,et al.  Video-Based Vehicle Counting Framework , 2019, IEEE Access.

[23]  Hong-Yuan Mark Liao,et al.  YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.

[24]  Omer Tanovic,et al.  Counting traffic using optical flow algorithm on video footage of a complex crossroad , 2010, Proceedings ELMAR-2010.

[25]  Shubhra Aich,et al.  Leaf Counting with Deep Convolutional and Deconvolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[26]  Mario Vento,et al.  Counting people by RGB or depth overhead cameras , 2016, Pattern Recognit. Lett..

[27]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[28]  Quoc V. Le,et al.  Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[29]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.