Real-time RGB-D based template matching pedestrian detection

Pedestrian detection is one of the most popular topics in computer vision and robotics. Considering challenging issues in multiple pedestrian detection, we present a real-time depth-based template matching people detector. In this paper, we propose different approaches for training the depth-based template. We train multiple templates for handling issues due to various upper-body orientations of the pedestrians and different levels of detail in depth-map of the pedestrians with various distances from the camera. And, we take into account the degree of reliability for different regions of sliding window by proposing the weighted template approach. Furthermore, we combine the depth-detector with an appearance based detector as a verifier to take advantage of the appearance cues for dealing with the limitations of depth data. We evaluate our method on the challenging ETH dataset sequence. We show that our method outperforms the state-of-the-art approaches.

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

[2]  Konrad Schindler,et al.  Improved Multi-Person Tracking with Active Occlusion Handling , 2009, ICRA 2009.

[3]  Xiaogang Wang,et al.  Pedestrian detection aided by deep learning semantic tasks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[5]  Bernt Schiele,et al.  Filtered channel features for pedestrian detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[7]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Bastian Leibe,et al.  Close-Range Human Detection and Tracking for Head-Mounted Cameras , 2012, BMVC.

[9]  Silvio Savarese,et al.  Ieee Transaction on Pattern Analysis and Machine Intelligence 1 a General Framework for Tracking Multiple People from a Moving Camera , 2022 .

[10]  Xiaogang Wang,et al.  Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[11]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.

[12]  Dennis Mitzel.  Close-Range Human Detection for Head-Mounted Cameras , 2012 .

[13]  Charless C. Fowlkes,et al.  Multiresolution Models for Object Detection , 2010, ECCV.

[14]  Ian Reid,et al.  fastHOG – a real-time GPU implementation of HOG , 2011 .

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

[16]  Bernt Schiele,et al.  Monocular 3D pose estimation and tracking by detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  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).

[18]  Anton van den Hengel,et al.  Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features , 2014, ECCV.

[19]  Shane Brennan,et al.  A Fast Stereo-based System for Detecting and Tracking Pedestrians from a Moving Vehicle , 2009, Int. J. Robotics Res..

[20]  Xiaogang Wang,et al.  Switchable Deep Network for Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Mayank Bansal,et al.  A real-time pedestrian detection system based on structure and appearance classification , 2010, 2010 IEEE International Conference on Robotics and Automation.

[22]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[24]  Rudolf Mester,et al.  Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming , 2008 .

[25]  Bastian Leibe,et al.  Real-Time Multi-Person Tracking with Time-Constrained Detection , 2011, BMVC.

[26]  Bastian Leibe,et al.  Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[27]  Matteo Munaro,et al.  Tracking people within groups with RGB-D data , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Bernt Schiele,et al.  Taking a deeper look at pedestrians , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).