Sports Video Classification with Deep Convolution Neural Network: A Test on UCF101 Dataset
M. Ramesh1, K. Mahesh2
1M. Ramesh, Department of Computer Science, Faculty of Science and Humanities, SRM IST, Kattankulathur, Chennai (Tamil Nadu), India.
Dr. K. Mahesh, Professor, Department of Computer Applications, Alagappa University, Karaikudi (Tamil Nadu), India.
Manuscript received on 18 July 2019 | Revised Manuscript received on 25 July 2019 | Manuscript Published on 01 August 2019 | PP: 24-27 | Volume-8 Issue-4S2, April 2019 | Retrieval Number: D10070484S219/19©BEIESP | DOI: 10.35940/ijeat.D1007.0484S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: In the present era, Deep Learning has been applied on a variety of problems from image processing to speech recognition. Convolution Neural Network (CNN) has been extensively used as a powerful classification model for image recognition problems. Video classification presents unique challenges but the problem related to video data is similar to image classification or an object detection problem. The main purpose of video classification in sports is to help the viewers to find the video of their own interest for training and improve the performance. The proposed work is a preliminary attempt to evaluate the performance of deep convolution neural network architectureson the ordered sequence of frames of the sports video. Video classification and video content analysis is one of the ongoing research areas in the field of computer vision. The classification of each frames are recorded and the majority vote of the frames are used to classify the video. UCF101 Video action database has been used for the classification problem.
Keywords: Convolutional Neural Network, Deep Learning, Video Classification, Sports Video, Video content Analysis.
Scope of the Article: Classification