SIFT and Tensor Based Object Detection and Classification in Videos Using Deep Neural Networks

Object classification in videos is very important for applications in automatic visual surveillance system. The process of classifying objects into predefined and semantically meaningful categories using its features is called object classification. As far as humans are concerned object classification in videos is a simple task but it is a complex and challenging task for machines due to different factors such as object size, occlusion, scaling, lightening etc. The need for analyzing video sequences resulted in the development of different object classification techniques. In this paper we propose a new model for detection and classification of objects in videos by incorporating Tensor features along with SIFT to classify the detected objects using Deep Neural Network(DNN. Deep Neural Networks are capable of handling large higher dimensional data with billions of parameters as like human brain. Simulation results obtained illustrate that the proposed classifier model produces more accurate results than the existing methods, which combines both SIFT and tensor features for feature extraction and DNN for classification. (C) 2016 The Authors. Published by Elsevier B.V.