Video Object Detection through Traditional and Deep Learning Methods
Sita M. Yadav1, Sandeep M. Chaware2
1Sita M Yadav*, Computer Department, AIT Pune, Research Scholar at PCCoE, Pune, Maharashtra, India.
2Dr. Sandeep M. Chaware, Computer Engineering Department, MMCOE, Pune, Research Guide at PCCoE, Pune, Maharashtra, India.
Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 1822-1826 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6833049420/2020©BEIESP | DOI: 10.35940/ijeat.D6833.049420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Object detection in videos is gaining more attention recently as it is related to video analytics and facilitates image understanding and applicable to . The video object detection methods can be divided into traditional and deep learning based methods. Trajectory classification, low rank sparse matrix, background subtraction and object tracking are considered as traditional object detection methods as they primary focus is informative feature collection, region selection and classification. The deep learning methods are more popular now days as they facilitate high-level features and problem solving in object detection algorithms. We have discussed various object detection methods and challenges in this paper.
Keywords: Video Object Detection, Deep Learning Methods