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A ROBUST TRACKING MOTION RECOGNITION WITH COMPLEX BACKGROUND FOR REAL TIME VIDEO SURVEILLANCE

1TARUN DHAR DIWAN, 2 FAYAZ AHMAD LONE and 3 UPASANA SINHA

Department of Engineering, Dr. C. V. Raman University, Bilaspur (C.G), India. Email IDs: tarunctech@gmail.com, fayazmca2009@gmail.com, upasanasinha@gmail.com

 

ABSTRACT

            In this paper, we aim to analyze multi-camera scenes by finding contextual tracking information of objects of interest. The work done until now shows the capabilities of multi-cue approaches: texture features and detection outcomes are used for tracking vehicles in tunnel surveillance. One of those is robustness of these techniques to changes in the environment: illumination, size of the objects and many others. Sometimes, additional video cameras are necessary to complement the surveillance information, especially for large scale applications. We aim to investigate new techniques and design new intelligent algorithms, which have to make use of partial information from several video sources. Effective performance evaluation is deemed important towards achieving successful Smart Video Surveillance Systems intelligent (smart) surveillance systems, which are now watching the video and providing alerts and content based search capabilities, make the video monitoring and investigation process scalable and effective. The programmed that analyze the video and provide alerts are commonly referred to as video analytics. These are responsible for turning video cameras from a mere data gathering tool into smart surveillance systems for proactive security. Smart surveillance systems have been enabled by the advances in computer vision, video analysis, pattern recognition and multimedia indexing technologies over the past decade. Additional video cameras are necessary to complement the surveillance information, especially for large scale applications. We aim to investigate new techniques and design new intelligent algorithms, which have to make use of partial information from several video sources. One of those is robustness of these techniques to changes in the environment: illumination, size of the objects and many others. Sometimes, additional video cameras are necessary to complement the surveillance information, especially for large scale applications. We aim to investigate new techniques and design new intelligent algorithms, which have to make use of partial information from several video sources. Object tracking data for further scene analysis and understanding.

Keywords: surveillance information, segmenting, Object tracking, significant percentage, auto-calibration, contextual tracking, pattern recognition.

 

International eJournal of Mathematical Sciences, Technology and Humanities

Volume 3, Issue 3, Pages:  1099 - 1105