To detect and Recognize Object from Videos for Computer Vision by Parallel Approach using Deep Learning

Abstract

Computer vision is the multidisciplinary domain extracts and analyses digital images in an automated manner. The application of computer vision is widespread and it ranges from agriculture to robotics. At present, computer vision adopts the concept of machine learning to build a model and solves classification problems. However, this technique becomes inefficient when it is directly applied to digital images as it ignores the structure and compositional nature of the images. Deep Convolutional Neural Network (CNN) acts as the best solution to traditional computer vision approaches as it learns to extract features from the raw images along with the classification process. In this paper, we present a deep learning based solution to computer vision problem. First, we define a CNN based approach to learn and extract features from the real time videos. Next, an extended linear support vector machine (SVM) classifier is used for object classification processes. Thus the proposed method make use of the combinational approach of the deep learning and machine learning to solve computer vision problems. Since deep CNN are massively parallel algorithms the application of CNN techniques with GPU forms the effective solution for computer vision problems. The experimental results are evaluated in terms performance, accuracy and simplicity measures.