Human Detection Using Surf And Sift Feature Extraction Methods In Different Color Spaces

Local feature matching has become a commonly used method to compare images. For tracking and human detection, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. two different types of image feature algorithms, Scale Invariant Feature Transform (SIFT) and the more recent Speeded Up Robust Features (SURF), have been used to compare the images. In this paper, we propose the use of a rich set of feature descriptors based on SIFT and SURF in the different color spaces.

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

[2]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Ramakant Nevatia,et al.  Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[4]  Pietro Perona,et al.  Multiple Component Learning for Object Detection , 2008, ECCV.

[5]  Larry S. Davis,et al.  Bilattice-based Logical Reasoning for Human Detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Larry S. Davis,et al.  A Pose-Invariant Descriptor for Human Detection and Segmentation , 2008, ECCV.

[7]  Patrick Sayd,et al.  Real-time human detection in urban scenes: Local descriptors and classifiers selection with AdaBoost-like algorithms , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[8]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  David A. Forsyth,et al.  Configuration Estimates Improve Pedestrian Finding , 2007, NIPS.

[10]  L. Gool,et al.  Interactive museum guide : fast and robust recognition of museum objects , 2006 .

[11]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Chu-Song Chen,et al.  Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages , 2008, IEEE Transactions on Image Processing.

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

[14]  Wei Zhang,et al.  Image Based Localization in Urban Environments , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

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

[16]  Ramakant Nevatia,et al.  Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Cordelia Schmid,et al.  Human Detection Based on a Probabilistic Assembly of Robust Part Detectors , 2004, ECCV.

[18]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

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

[20]  Luc Van Gool,et al.  Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[21]  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).

[22]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[25]  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).