Person Detection by Low-rank Sparse Aggregate Channel Features

Human detection in the video has several applications in security and surveillance. Human detection using video is desired to be robust against illumination, occlusions, scale, translation and view angle variations. In this paper, we develop an approach which can improve the performance of the aggregate channel feature for a high view angle. The foreground is estimated using a frame differences approach to identify the location of moving objects in the static camera scene. The sparse basis is included in the aggregate channel feature vector to describe the foreground region of each frame of the video. This approach provides better miss rate versus false positive per image as compared to the existing aggregate channel feature and histogram of the oriented gradient.

[1]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Leslie M. Collins,et al.  Viewpoint Adaptation for Rigid Object Detection , 2017, ArXiv.

[4]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[5]  P. Rudol,et al.  Human Body Detection and Geolocalization for UAV Search and Rescue Missions Using Color and Thermal Imagery , 2008, 2008 IEEE Aerospace Conference.

[6]  Leslie M. Collins,et al.  Viewpoint Adaptation for Person Detection , 2016 .

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

[8]  Toby P. Breckon,et al.  Real-time people and vehicle detection from UAV imagery , 2011, Electronic Imaging.

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

[10]  Pietro Perona,et al.  Pedestrian detection: A benchmark , 2009, CVPR.

[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]  Alvaro Fernandez-Rincon,et al.  Robust people detection using depth information from an overhead Time-of-Flight camera , 2017, Expert Syst. Appl..

[13]  Mubarak Shah,et al.  Geometric Constraints for Human Detection in Aerial Imagery , 2010, ECCV.

[14]  Piotr Dollár,et al.  Crosstalk Cascades for Frame-Rate Pedestrian Detection , 2012, ECCV.