Robust Human Detection with Low Energy Consumption in Visual Sensor Network

In this paper, we try to address the difficult problem of detecting humans robustly with low energy consumption in the visual sensor network. The proposed method contains two parts: one is an ESOBS (Enhanced Self-Organizing Background Subtraction) based foreground segmentation module to obtain active areas in the observed area from the visual sensor; the other is a HOG (Histograms of Oriented Gradients) based detection module to detect the appearance shape from the foreground areas. Moreover, we create a large pedestrian dataset according to the specific scene in visual sensor networks. Numerous experiments are conducted. The experimental results show the effectiveness of our method.

[1]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Dariu Gavrila,et al.  Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle , 2007, International Journal of Computer Vision.

[4]  Mei-Chen Yeh,et al.  Fast Human Detection Using a Cascade of Histograms of Oriented Gradients , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Wei Zhang,et al.  Real-time Accurate Object Detection using Multiple Resolutions , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[8]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Larry S. Davis,et al.  Human detection using partial least squares analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[10]  Brian C. Lovell,et al.  An efficient and robust sequential algorithm for background estimation in video surveillance , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Shuicheng Yan,et al.  Discriminative local binary patterns for human detection in personal album , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  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).

[13]  H. Bischof,et al.  Fast human detection in crowded scenes by contour integration and local shape estimation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Hong Liu,et al.  An effective background reconstruction method for complicated traffic crossroads , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Takeo Kanade,et al.  People detection based on co-occurrence of appearance and spatio-temporal features , 2010 .

[16]  Zengqin Zhao,et al.  Solutions and Green's functions for some linear second-order three-point boundary value problems , 2008, Comput. Math. Appl..