Robust Movement-Specific Vehicle Counting at Crowded Intersections

With the demands of intelligent traffic, vehicle counting has become a vital problem, which can be used to mitigate traffic congestion and elevate the efficiency of the traffic light. Traditional vehicle counting problems focus on counting vehicles in a single frame or consecutive frames. Nevertheless, they are not expected to count vehicles by movements of interest (MOI), which can be pre-defined by all possible states of vehicles, combining different lanes and directions. In this paper, we mainly focus on movement-specific vehicle counting problem. A detection-tracking-counting (DTC) framework is applied, which detects and tracks objects in the region of interest (ROI), then counts those tracked trajectories by movements. To be specific, we propose the detection augmentation method and the Mahalanobis distance smoothness method to improve the multi-object tracking performance. For vehicle counting, a shape-based movement assignment method is carefully designed to categorize each trajectory by movements. Experiments are conducted on both the AICity 2020 Track-1 Dataset and the Vehicle-Track Dataset, which is built in this paper. Experimental results show the effectiveness and efficiency of our method.

[1]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[3]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Konrad Schindler,et al.  Online Multi-Target Tracking Using Recurrent Neural Networks , 2016, AAAI.

[5]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[6]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[9]  Jiwen Lu,et al.  Collaborative Deep Reinforcement Learning for Multi-object Tracking , 2018, ECCV.

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

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

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

[14]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[15]  Nenghai Yu,et al.  Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

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

[18]  Bernt Schiele,et al.  Subgraph decomposition for multi-target tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Silvio Savarese,et al.  Learning to Track: Online Multi-object Tracking by Decision Making , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[20]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[21]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[22]  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.

[23]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Silvio Savarese,et al.  Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[25]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

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

[29]  Pascal Fua,et al.  Tracking Interacting Objects Using Intertwined Flows , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Pascal Fua,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiple Object Tracking Using K-shortest Paths Optimization , 2022 .

[31]  Ramakant Nevatia,et al.  Global data association for multi-object tracking using network flows , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Silvio Savarese,et al.  Recurrent Autoregressive Networks for Online Multi-object Tracking , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[33]  Jean-Philippe Thiran,et al.  Counting Pedestrians in Video Sequences Using Trajectory Clustering , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[34]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[37]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Huadong Ma,et al.  Robust Head-Shoulder Detection by PCA-Based Multilevel HOG-LBP Detector for People Counting , 2010, 2010 20th International Conference on Pattern Recognition.

[39]  Hua Yang,et al.  Online Multi-Object Tracking with Dual Matching Attention Networks , 2018, ECCV.

[40]  Luo Juan,et al.  A comparison of SIFT, PCA-SIFT and SURF , 2009 .

[41]  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.

[42]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[43]  Xu Gao,et al.  OSMO: Online Specific Models for Occlusion in Multiple Object Tracking under Surveillance Scene , 2018, ACM Multimedia.

[44]  Weixing Wang,et al.  Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy , 2018, Soft Comput..

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

[46]  Afshin Dehghan,et al.  GMMCP tracker: Globally optimal Generalized Maximum Multi Clique problem for multiple object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  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.

[48]  Afshin Dehghan,et al.  Target Identity-aware Network Flow for online multiple target tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[49]  Jieping Ye,et al.  Object Detection in 20 Years: A Survey , 2019, Proceedings of the IEEE.

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

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