Moving Object Classification in a Video Sequence

Now-a-day's monitoring the objects (human beings, animals, buildings, vehicles etc.,) in a video is a major issue in the areas such as airports, banks, military installations etc., Classification of objects in a video involves the process of searching, retrieving and indexing. This process is implemented by extracting the features such as color, texture and shape. This technique is difficult but it has its limitations at various situations. Techniques such as edge detection using various filters, edge detection operators, CBIR (Content Based Image Retrieval) and Bag-of visual words are used to classify videos into fixed broad classes which would assist searching and indexing using semantic keywords. The proposed approach extracts three types of features viz. Color features using RGB and HSV histograms, Structure features using HoG, DHoG, Harris, Prewitt, LoG operators and Texture features using LBP, Fourier and Wavelet transforms. Additionally BoV is used for improving the classification performance and accuracy. SVM, Bagging, Boosting, J48 classifiers is used for classification.