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Research Scholer

Prashant Patil

Email :

Phone : 9637817317

Address : CVPR Lab, IIT Ropar, India.

Research Topic : Moving Object Detection

Current Position : Research Scholar, CVPR Lab, EED.

Website :


B.Tech Pune University, Pune, MAHARASHTRA
Ph.D CVPR Lab, Department of Electrical Engineering, IIT Ropar, India.

About Research Topic

The rapid increase of video records is the motivation behind various applications based on automatic video analysis. To find visual moving objects by measuring the dissimilarity between foreground and background region is the process of Moving Object Detection (MOD). Motion detection is one of the prime step for automatic video analysis like automated video surveillance, automated traffic monitoring and automated driver assistance system, etc. General assumption is that the video recorded by static camera have motionless background and only moving foregrounds. But, practically background may also contain some local motion like waving trees and foreground may show infrequent motion, etc. Also, MOD in different practical scenarios like dynamic background, weather degraded, shadow, etc. is a difficult task. So, there is dire need of novel approach for MOD which is able to detect moving objects in different practical scenarios with less computational complexity.

My Research Objectives are as follows :

  • propose a novel framework for Moving Object Detection
  • detect moving objects in different practical scenarios
  • less computational complexity

Introductory Video About Research Topic


Journal Prashant Patil and Subrahmanyam Murala, "MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection", in IEEE Transactions on Intelligent Transportation Systems,. (pdf) (Impact factor 5.744 )
Conf Prashant Patil and Subrahmanyam Murala, "FgGAN: A Cascaded Unpaired Learning for Background Estimation and Foreground Segmentation", in IEEE WACV-2019, pp. 1770-1778, Waikoloa Village, Hawaii, USA, 2019,. (pdf)
Conf Prashant Patil, Omkar Thawakar, Akshay Dudhane and Subrahmanyam Murala "Motion Saliency Based Generative Adversarial Network For Underwater Moving Object Segmentation", in IEEE ICIP-2019, Taipei, Taiwan, 2019,. (pdf)
Conf Omkar Thawakar, Prashant Patil, Akshay Dudhane, Subrahmanyam Murala and Uday Kulkarni "Image and Video Super Resolution using Recurrent Generative Adversarial Network", in IEEE AVSS-2019, Taipei, Taiwan, 2019,. (pdf)
Conf Sachin Chaudhary, Akshay Dudhane, Prashant Patil and Subrahmanyam Murala "Pose Guided Dynamic Image Network for Human Action Recognition in Person Centric Videos", in IEEE AVSS-2019, Taipei, Taiwan, 2019,. (pdf)
Conf Prashant Patil , Subrahmanyam Murala, Abhinav Dhall and Sachin Chaudhary "MsEDNet: Multi-Scale Deep Saliency Learning for Moving Object Detection", in IEEE SMC-2018, Miyazaki, Japan, 2018,. (pdf)