Enhanced Object Representation on Moving Objects Classification

Abstract

A feature representation approach is proposed for discriminative features extraction and this object representation tend to handle the large amount of local features in feature correspondence. Object representation with shape and color feature tends to certify the strength of proposed feature extraction method. In the proposed method, HOG are extracted on 300 corner points which are the strongest points on detected corners and these points are supposed as in one block to get the HOG vector. As a second portion of feature extraction, the moments on HSI are extracted on each separated channel. The proposed feature extraction method is tested intensively on the different sequences of the Online Benchmark Tracking dataset, CAVIAR Test Case Scenarios and Change Detection dataset (CDnet 2014) with the comparison of other related feature extraction methods. Classification of proposed approach receives 98.1%, 93.8%, 96.8%, 97.7% and 90.5% for walking, crossing, walk1, pedestrians and twopositionPTZCam respectively.