Link Speed Estimation for Traffic Flow Modelling Based on Video Feeds from Monocular Cameras

In this paper, we present a reliable and scalable approach for real-time estimation of link speeds (i.e., traffic speeds on specific road segments) based on video feeds coming from monocular cameras. We detect and track vehicles of specific types, identify anchor points (or keypoints) on them, compute their poses, and use this information to estimate their speeds. We use deep learning methods for vehicle detection, tracking, keypoint detection and localization, and traditional 3D pose estimation techniques for which precise mathematical solutions are available. Thus, our approach exploits the best of both worlds. The proposed approach does not require any physical measurements (extrinsics) in the road scene, making it scalable and easy to install. Our results on video feeds from Bangalore, India, show that the method is able to generalize well for cameras mounted on street light poles, congested traffic situations, and various lighting conditions. Thus, the solution is suitable for emerging market scenarios where traffic tends to be chaotic and dense, and mounting speed sensors or strategically located downward-facing cameras is not feasible. The code and dataset for this work are being made available2.2https://github.com/ShantamShorewala/vehicle-speed-keypoint-data

[1]  Daniel J. Dailey,et al.  An algorithm to estimate mean traffic speed using uncalibrated cameras , 2000, IEEE Trans. Intell. Transp. Syst..

[2]  Danang Wahyu Wicaksono,et al.  Speed Estimation On Moving Vehicle Based On Digital Image Processing , 2017 .

[3]  J. Sharmila Rani,et al.  Tracking and Speed Estimation of Moving Vehicle for Traffic Surveillance System , 2016 .

[4]  Anil Y. G. Rao,et al.  Real-time speed estimation of vehicles from uncalibrated view-independent traffic cameras , 2015, TENCON 2015 - 2015 IEEE Region 10 Conference.

[5]  Manjari Gupta,et al.  Vehicle Tracking And Speed Estimation Using Optical Flow , 2011 .

[6]  Rama Chellappa,et al.  A Semi-Automatic 2D Solution for Vehicle Speed Estimation from Monocular Videos , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  D.J. Dailey,et al.  A novel technique to dynamically measure vehicle speed using uncalibrated roadway cameras , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[8]  Xingyi Zhou,et al.  Objects as Points , 2019, ArXiv.

[9]  Ekta Patel,et al.  Speed Determination of Moving Vehicles using Lucas-Kanade Algorithm , 2012 .

[10]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[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]  Dong Liu,et al.  Deep High-Resolution Representation Learning for Human Pose Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Adam Herout,et al.  Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement , 2017, Comput. Vis. Image Underst..

[14]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[15]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[16]  Guilin Zhang,et al.  Vehicle Pose and Shape Estimation Through Multiple Monocular Vision , 2018, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[17]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[19]  N. Dinesh Reddy,et al.  Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Qi Tian,et al.  Highway traffic information extraction from Skycam MPEG video , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[21]  Jianliang Tang,et al.  Complete Solution Classification for the Perspective-Three-Point Problem , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Jenq-Neng Hwang,et al.  The 2018 NVIDIA AI City Challenge , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[23]  Sedat Dogan,et al.  Real Time Speed Estimation of Moving Vehicles from Side View Images from an Uncalibrated Video Camera , 2010, Sensors.

[24]  Adam Herout,et al.  Automatic Camera Calibration for Traffic Understanding , 2014, BMVC.