Computer Vision Based Human Detection

From still images human detection is challenging and important task for computer vision-based researchers. By detecting Human intelligence vehicles can control itself or can inform the driver using some alarming techniques. Human detection is one of the most important parts in image processing. A computer system is trained by various images and after making comparison with the input image and the database previously stored a machine can identify the human to be tested. This paper describes an approach to detect different shape of human using image processing. This thesis mainly based on shape based detection. Shape of the input image is extracted using a operator namely cany operator. Different images are used to train up the system. Then after training the system with the input image when a test image is provided to detect, test image is then compared with the database. If a certain threshold value is found then the test image is considered as the specific human. The average accuracy and precision rate achieved by the system is above 93%. Keyword: Computer Vision, Human Detection, Edge detection

[1]  D.M. Gavrila,et al.  Vision-based pedestrian detection: the PROTECTOR system , 2004, IEEE Intelligent Vehicles Symposium, 2004.

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

[3]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[5]  Robert E. Schapire,et al.  The Boosting Approach to Machine Learning An Overview , 2003 .

[6]  Dariu Gavrila,et al.  Real-time object detection for "smart" vehicles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[8]  Visvanathan Ramesh,et al.  Order consistent change detection via fast statistical significance testing , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Visvanathan Ramesh,et al.  An Intensity-augmented Ordinal Measure for Visual Correspondence , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

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

[12]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[14]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

[16]  Raj Gupta,et al.  SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor , 2008, ECCV.

[17]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[18]  Cordelia Schmid,et al.  Learning to Parse Pictures of People , 2002, ECCV.

[19]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[20]  Raj Gupta,et al.  Robust order-based methods for feature description , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[22]  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).

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

[24]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.