Fast and Robust Algorithm of Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo,2011 Goal • detecting and tracking multiple moving objects • real-time detecting • robustness against the environmental influences and the speed Outline • Introduction • Previous Methods • Detecting Moving Objects – Extraction of Moving Objects – Grouping Moving Objects • Tracing Moving Objects • Implementation and Experiment • Conclusions Introduction • In the traditional systems that a person should always monitor video. • intelligent video surveillance systems are high-cost and low-efficiency • Environment affects a lot. • This paper propose a method detecting and tracking multiple moving objects in real-time. Previous Methods • particle filter ,extended Kalman filter • Background modeling (BM) or the Gaussian mixture model (GMM) • gray-scale BM shows the image information is excessively attenuated. Extraction of Moving Objects • Using RGB color BM instead of gray-scale BM • Each pixels will compare with previous pixels in little group. • If it is stationary, the pixels will be black. • The parameter δ is proposed to overcome the sensitivity problem . • δ would be different on different camera. Extraction of Moving Objects Extraction of Moving Objects Extraction of Moving Objects Grouping Moving Objects • The individual tracking of neighboring or overlapping objects requires a lot of computational capacity . • The 4-directional blob-labeling is employed to group moving objects. Grouping Moving Objects • Contour Tracing Grouping Moving Objects • its initial search position is set to be d+2 (mod 8) Tracing Moving Objects Tracing Moving Objects Implementation and Experiment • The 33Mbit IP camera provides the input image with 704x480 pixels. • The surveillance image is transmitted through Internet. • 2.66GHz CPU and 4GB RAM PC for the image signal processing and the proposed algorithm. Implementation and Experiment Implementation and Experiment Implementation and Experiment Conclusions • Real-time detecting and tracing • Only for fixed camera. • Future works can be on predicted position.
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