Moving Object Detection using Differential Evolution
Amlan Raychaudhuri1, Arkadev Roy2, Ashesh Das3, Gourav Kumar Shaw4, Pratik Kumar Mitra5
1Amlan Raychaudhuri, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
2Arkadev Roy, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
3Ashesh Das, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
4Gourav Kumar Shaw, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
5Pratik Kumar Mitra, Department of CSE, B. P. Poddar Institute of Management & Technology, Kolkata, India.
Manuscript received on May 01, 2014. | Revised Manuscript received on May 05, 2014. | Manuscript published on May 05, 2014. | PP: 170-173 | Volume-4 Issue-2, May 2014. | Retrieval Number: B2244054214/2014©BEIESP
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©The Authors. Published By: 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: Moving Object detection is the process of detecting a change in position of an object relative to its surroundings or the change in the surroundings relative to an object. Different complex algorithms are employed to detect a moving object in a video. It has large number of applications in video surveillance and other security systems that are used to process video information. We have achieved it using Differential Evolution (DE). The proposed method is successfully tested over two video sequences.
Keywords: Clustering, Differential Evolution, Moving Object Detection, Temporal video segmentation.