ABSTRACT

Object detection has seen huge process in recent years. Object detection is the process to determine whether there are instances of interest in an image. The main difficulty of object detection arises from high variability in appearance among objects of the same class. An automatic object detection system. must also to determine the presence and absence of objects with different sizes and viewpoints under complex background clutters. An object detection approach using histogram features is proposed. The Histogram of Oriented Gradient (HOG) is a feature descriptor used in computer vision and image processing. The influence of each stage of the computation on performance, concluding that fine scale gradients, fine orientation binning, relatively coarse binning and high quality local contrast normalization in overlapping descriptor blocks are all important for good results .Experimental results show that the proposed approach is for effective object detection has better accuracy with less processing time consumption rather than existing methods.