An empirical study of visual features for part based model

Object detection is a fundamental task in computer vision. Deformable part based model has achieved great success in the past several years, demonstrating very promising performance. Many papers emerge on part based model such as structure learning, learning more discriminative features. To help researchers better understand the existing visual features' potential for part based object detection and promote the deep research into part based object representation, we propose an evaluation framework to compare various visual features' performance for part based model. The evaluation is conducted on challenging PASCAL VOC2007 dataset which is widely recognized as a benchmark database. We adopt Average Precision (AP) score to measure each detector's performance. Finally, the full evaluation results are present and discussed.

[1]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Alexei A. Efros,et al.  An empirical study of context in object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.

[5]  Long Zhu,et al.  Active Mask Hierarchies for Object Detection , 2010, ECCV.

[6]  Bernt Schiele,et al.  Discriminative structure learning of hierarchical representations for object detection , 2009, CVPR.

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

[8]  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).

[9]  Bernt Schiele,et al.  Automatic discovery of meaningful object parts with latent CRFs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Fatih Murat Porikli,et al.  Human Detection via Classification on Riemannian Manifolds , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Frédéric Jurie,et al.  Groups of Adjacent Contour Segments for Object Detection , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Bill Triggs,et al.  Feature Sets and Dimensionality Reduction for Visual Object Detection , 2010, BMVC.

[13]  E. Rückert.  Detecting Pedestrians by Learning Shapelet Features , 2007 .

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

[15]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[16]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[17]  William T. Freeman,et al.  Latent hierarchical structural learning for object detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[19]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[21]  David A. McAllester,et al.  Cascade object detection with deformable part models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.