An Improved Approach for Video Surveillance Using Kalman Filtering and Frame Rate Optimization


Authors : Chitra Singh Shekhawat, Hardayal S. Shekhawat, Maninder Singh Nehra

Volume/Issue : Volume 2 - 2017, Issue 5 - May

Google Scholar : https://goo.gl/g3ICxF

Scribd : https://goo.gl/1NGXT3

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

Object detection and tracking are essential and challenging obligations in plenty of device vision packages equal to surveillance, car navigation, and self maintaining robot navigation. Video surveillance in dynamic surroundings, mostly for humans and cars, is, genuinely, one of the important, rigorous topics in computer vision. It’s a key technology to combat in competition to terrorism, crime, public protect and for green control of visitors. The model entails designing of the capable video surveillance machine in complex environments. In video surveillance, detection of transferring items from a video is necessary for object detection, target tracking, and conduct. The proposed model deploys a Kalman Filtering approach for object tracking and frame rate optimization to improve the quality of performance.

Keywords : video surveillance, object tracking, mean square error

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