Learning to Recognize Pedestrian Attribute

Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given. In this study, we present an alternative approach that exploits the context of neighboring pedestrian images for improved attribute inference compared to the conventional SVM-based method. In addition, we conduct extensive experiments to evaluate the informativeness of background and foreground features for attribute recognition. Experiments are based on our newly released pedestrian attribute dataset, which is by far the largest and most diverse of its kind.

[1]  Tao Mei,et al.  Joint multi-label multi-instance learning for image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Philip S. Yu,et al.  Clustering through decision tree construction , 2000, CIKM '00.

[3]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[5]  Shaogang Gong,et al.  Person Re-identification by Attributes , 2012, BMVC.

[6]  Shaogang Gong,et al.  Constructing Robust Affinity Graphs for Spectral Clustering , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Shengcai Liao,et al.  Pedestrian Attribute Classification in Surveillance: Database and Evaluation , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[8]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[9]  Li Yu,et al.  Video Analytics for Business Intelligence , 2012, Video Analytics for Business Intelligence.

[10]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[11]  Shaogang Gong,et al.  Video Synopsis by Heterogeneous Multi-source Correlation , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Andrew Blake,et al.  Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Yun Fu,et al.  Gender recognition from body , 2008, ACM Multimedia.

[14]  Xiaogang Wang,et al.  Pedestrian Parsing via Deep Decompositional Network , 2013, 2013 IEEE International Conference on Computer Vision.

[15]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[16]  Xiaoou Tang,et al.  Pedestrian Attribute Recognition At Far Distance , 2014, ACM Multimedia.

[17]  Cordelia Schmid,et al.  Learning Color Names for Real-World Applications , 2009, IEEE Transactions on Image Processing.

[18]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Shaogang Gong,et al.  The Re-identification Challenge , 2014, Person Re-Identification.

[20]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.