Fast Cyclist Detection by Cascaded Detector and Geometric Constraint

In this paper we present a vision-based detection system for cyclists. We build cascaded detectors with different classifiers and shared features to detect cyclists from multiple viewpoints. To improve the performance, we reveal the dependence between the size and the position of an object in the image by a regression method. We also explore the applications of this geometric constraint with different camera setups. Based on experiments we demonstrate that our detector is suitable for real time applications.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

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

[3]  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.

[4]  Martin Lauer,et al.  Fast and Robust Cyclist Detection for Monocular Camera Systems , 2016 .

[5]  Darcy M. Bullock,et al.  Statewide Wireless Communications Project; Volume 2: Inductive Loop Detection of Bicycles and Inductive Loop Signature Processing for Travel Time Estimation , 2008 .

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

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

[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]  Takashi Morie,et al.  Bicycle detection using pedaling movement by spatiotemporal Gabor filtering , 2010, TENCON 2010 - 2010 IEEE Region 10 Conference.

[11]  Christoph Stiller,et al.  Non-parametric lane estimation in urban environments , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[12]  D A Noyce,et al.  AN EVALUATION OF TECHNOLOGIES FOR AUTOMATED DETECTION AND CLASSIFICATION OF PEDESTRIANS AND BICYCLISTS , 2002 .

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

[14]  Junjie Yan,et al.  The Fastest Deformable Part Model for Object Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Dariu Gavrila,et al.  Integrated pedestrian classification and orientation estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[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 3D bicycle tracking using deformable part model and Interacting Multiple Model filter , 2011, 2011 IEEE International Conference on Robotics and Automation.

[18]  Yi Zhang,et al.  The study of the detection of pedestrian and bicycle using image processing , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

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

[20]  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).

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

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