Fast and Robust Cyclist Detection for Monocular Camera Systems

Cyclist detection is an important task for automobile industries. In this paper we present a vision based system for cyclist detection. We build cascade detectors for cyclists in different viewpoints and part filters to deal with partial occlusions. To improve the performance, geometry based ROI extraction method is integrated. Additionally, a Kalman filter in combination with optical flow is also applied to estimate cyclists’ trajectories and to stabilize detections along image sequence.

[1]  Tong Li,et al.  An effective crossing cyclist detection on a moving vehicle , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[2]  Jitendra Malik,et al.  Efficient shape matching using shape contexts , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Bastian Leibe,et al.  Efficient Use of Geometric Constraints for Sliding-Window Object Detection in Video , 2011, ICVS.

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

[5]  Bernt Schiele,et al.  New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[7]  Tarak Gandhi,et al.  Pedestrian Protection Systems: Issues, Survey, and Challenges , 2007, IEEE Transactions on Intelligent Transportation Systems.

[8]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  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).

[11]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[15]  Scott Rogers,et al.  Counting bicycles using computer vision , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

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

[17]  Paul E. Rybski,et al.  Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[18]  Mohan M. Trivedi,et al.  Fast and Robust Object Detection Using Visual Subcategories , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[19]  Pavlina D. Konstantinova,et al.  An Accelerated IMM JPDA Algorithm for Tracking Multiple Manoeuvring Targets in Clutter , 2002, Numerical Methods and Application.

[20]  Bernt Schiele,et al.  A Performance Evaluation of Single and Multi-feature People Detection , 2008, DAGM-Symposium.

[21]  MalikJitendra,et al.  Efficient Shape Matching Using Shape Contexts , 2005 .

[22]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[23]  E. Rückert.  Detecting Pedestrians by Learning Shapelet Features , 2007 .