Moving Object Detection and Recognition

Abstract

Detection of moving object is an active research topic in computer vision applications, like people counting, intruder detection, object tracking. For detection of moving object we are using background subtraction technique. In this thesis, we are dealing with Robust Principal Component Analysis which decomposes a given data matrix into low-rank component and sparse component. Low-rank component gives us the background portion whereas the sparse one gives the required foreground object. The limitation of classical PCA, i.e. it is not applicable when more number of outliers are present, is overcome by RPCA. Here RPCA is being solved by using Principal Component Pursuit (RPCA-PCP) under the ideal conditions, i.e. video is noise free and static camera being used. PCP imposed the low-rank component being exactly low-rank and the sparse component being exactly sparse but the observations such as in video surveillance are often corrupted by noise affecting every entry of the data matrix. In those situations PCP fails and it is being solved by using Stable Principal Component Analysis which assumes that observation matrix is summation of Low-rank component, sparse component, and the noise. A comparative analysis is being done by evaluating Recall, Precision of different video sequence using both RPCA-PCP, and RPCA-SPCP. After this for recognition purpose, CBIR is being used. Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) are used to find the feature vectors. Then query matching is done to recognize the object.