Evaluation of Human Detection Algorithms in Image Sequences

This paper deals with the general evaluation of human detection algorithms. We first present the algorithms implemented within the CAPTHOM project dedicated to the development of a vision-based system for human detection and tracking in an indoor environment using a static camera. We then show how a global evaluation metric we developped for the evaluation of understanding algorithms taking into account both localization and recognition precision of each single interpretation result, can be a useful tool for industrials to guide them in the elaboration of suitable and optimized algorithms.

[1]  Abderrahim Elmoataz,et al.  Image and Signal Processing, 4th International Conference, ICISP 2010, Trois-Rivières, QC, Canada, June 30-July 2, 2010. Proceedings , 2010, ICISP.

[2]  Gerd Kortuem,et al.  Smart Sensing and Context, Second European Conference, EuroSSC 2007, Kendal, England, UK, October 23-25, 2007, Proceedings , 2007, EuroSSC.

[3]  Baptiste Hemery,et al.  Evaluation Protocol for Localization Metrics , 2008, ICISP.

[4]  Thierry Pun,et al.  Performance evaluation in content-based image retrieval: overview and proposals , 2001, Pattern Recognit. Lett..

[5]  Hermann Ney,et al.  Improving a Discriminative Approach to Object Recognition Using Image Patches , 2005, DAGM-Symposium.

[6]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  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).

[8]  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.

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

[10]  Baptiste Hemery,et al.  Subjective evaluation of image understanding results , 2010, 2010 18th European Signal Processing Conference.

[11]  Jean-Michel Jolion,et al.  Object count/area graphs for the evaluation of object detection and segmentation algorithms , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[12]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  Hélène Laurent,et al.  Vision-Based System for Human Detection and Tracking in Indoor Environment , 2010, Int. J. Soc. Robotics.

[15]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[16]  Hélène Laurent,et al.  Review and evaluation of commonly-implemented background subtraction algorithms , 2008, 2008 19th International Conference on Pattern Recognition.

[17]  Baptiste Hemery,et al.  Evaluation metric for image understanding , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[18]  Ihsin T. Phillips,et al.  Empirical Performance Evaluation of Graphics Recognition Systems , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Pierre David,et al.  A Sensor Placement Approach for the Monitoring of Indoor Scenes , 2007, EuroSSC.

[20]  Neil A. Thacker,et al.  Performance characterization in computer vision: A guide to best practices , 2008, Comput. Vis. Image Underst..

[21]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[22]  Baptiste Hemery,et al.  Evaluation Protocol for Localization Metrics Application to a Comparative Study , 2008 .