Exploiting domain constraints for exemplar based bus detection for traffic scheduling

We describe a computer vision application to adaptive traffic signal control. Adaptive traffic signal control systems allocate green time at an intersection dynamically in response to sensed incoming traffic flows to continually optimize overall throughput. Existing video-based systems are limited to the use of vehicle presence and volume (count) data, and since it is not possible to distinguish between different types of vehicles, optimization opportunities can be missed. We propose to detect specific types of vehicles, such as buses, so that they can be assigned higher priority when appropriate and the overall effectiveness of the adaptive signal system can be improved. We base our system design on current visual recognition technology (HOG SVM) and exploit configuration constraints specific to this application, such as knowledge about the anticipated scale of the vehicles. The challenge is to be robust to varying illumination and weather conditions, occlusions from other vehicles, and large variations in scale while producing recognition results in real-time. We show results on challenging data from traffic cameras under different observation conditions and at varying ranges.

[1]  R. Schweiger,et al.  Multi-class Object Detection in Vision Systems Using a Hierarchy of Cascaded Classifiers , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[2]  B. P. Shrestha,et al.  Using global positioning system for bus priority in London: traffic signals close to bus stops , 2007 .

[3]  Terrance E. Boult,et al.  Multi-attribute spaces: Calibration for attribute fusion and similarity search , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Yang Cai,et al.  Adaptive feature annotation for large video sensor networks , 2013, J. Electronic Imaging.

[5]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[6]  F. Suard,et al.  Pedestrian detection using stereo-vision and graph kernels , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[7]  Stephen F. Smith,et al.  Smart Urban Signal Networks: Initial Application of the SURTRAC Adaptive Traffic Signal Control System , 2013, ICAPS.

[8]  Vishnu Naresh Boddeti,et al.  Advances in correlation filters: vector features, structured prediction and shape alignment , 2012 .

[9]  Stephen F. Smith,et al.  Coping with Real-World Challenges in Real-Time Urban Traffic Control , 2014 .

[10]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  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).

[12]  Alexei A. Efros,et al.  Exemplar-based Representations for Object Detection, Association and Beyond , 2011 .

[13]  B. Zavidovique,et al.  A context-dependent vision system for pedestrian detection , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[14]  A. Zelinsky,et al.  3D vision sensing for improved pedestrian safety , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[15]  Massimo Bertozzi,et al.  Shape-based pedestrian detection , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[16]  Luo Donghua.  A Bus Video Detection Algorithm Based on Geometry and Color , 2005 .

[17]  Philippe Martinet,et al.  Cars detection and tracking with a vision sensor , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[18]  Xu Sun,et al.  Bus detection based on sparse representation for transit signal priority , 2013, Neurocomputing.

[19]  Paul E. Rybski,et al.  Real-time pedestrian detection with deformable part models , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[20]  Sergio A. Velastin,et al.  Vehicle localisation and classification in urban CCTV streams , 2009 .

[21]  David A. McAllester,et al.  Object Detection with Grammar Models , 2011, NIPS.

[22]  K J Fehon,et al.  TRANSIT SIGNAL PRIORITY FOR SILICON VALLEY BUS RAPID TRANS , 2003 .

[23]  Tim Ellis,et al.  Tracking using colour information , 1994 .

[24]  Alexei A. Efros,et al.  Ensemble of exemplar-SVMs for object detection and beyond , 2011, 2011 International Conference on Computer Vision.

[25]  Alvaro Pardo,et al.  Bus Detection for Intelligent Transport Systems Using Computer Vision , 2013, CIARP.

[26]  Hua Yang,et al.  A Robust Bus Detection and Recognition Method Based on 3D Model and LSD Method for Public Security Road Crossing Application , 2012, IFTC.