Video Object Segmentation and Tracking Using GMM and GMM-RBF Method for Surveillance System

Now a day’s computer vision has been applied to every organisation. Such that the all in security systems, computers are widely used regarding to this the security purpose every organisation are used different monitoring system i.e. surveillance system, suspicious monitoring system etc. Object tracking and explanation is the definitive purpose of many video processing systems. The two critical, low-level computer vision tasks that have been undertaken in this work are: Foreground-Background Segmentation and Object Tracking. In surveillance system cameras capture the footage for tracking suspicious movement in organisation, in this condition the videos prepare with the help of surveillance cameras the most difficult task is to tracking the object from the video and make the another image so that image should be vague to identification. Generally the surveillance system work We use a stochastic model of the background and also adapt the model through time. This adaptive nature is essential for long-term surveillance applications, particularly when the background composition or intensity distribution changes with time. In such cases, concept of a static reference background would no longer make sense. DOI: 10.17762/ijritcc2321-8169.15062

Uncertainty-aware video visual analytics of tracked moving objects

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009