A Fast Moving Object Detection Method via Local Neighborhood Similarity
Electron. & Inf. Eng. Coll., Henan Univ. of Sci. & Technol., Luoyang, ChinaDOI: 10.1109/ICMA.2009.5244841 Conference: Mechatronics and Automation, 2009. ICMA 2009. International Conference on
Source: IEEE Xplore
Background subtraction is widely used in moving object detection. Pixel-based methods are sensitive to the nonstationary change of the scenes. Region-based approaches allow only coarse detection of the moving objects. In this paper, a novel algorithm based on local neighborhood similarity is proposed. Integrate the similarity of its surrounding pixels with the background model, when a pixel needs to be judged. The performance of the proposed method is evaluated by a series of indoor and outdoor experiments. Compared with the current widely used Mixture of Gaussian, the proposed algorithm in this paper achieved the perfect results in object detection and extraction.
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ABSTRACT: This paper presents a method to accurately detect and monitor ships within the area of interest. It is an advanced version of the previous works done regarding moving ship detection and tracking. The proposed tracking scheme is based on the characteristics of both sea and ship, which includes: background information and local position of the ship. Background subtraction and registration is achieved using morphological ‘Open’ operation and the ships are located using their edge information. The experimental results demonstrate robust and real-time ship detection and tracking with 98.7% detection rate. The proposed algorithm will be useful in coastal surveillance and monitoring applications.Signal Processing and Multimedia - International Conferences, SIP and MulGraB 2010, Held as Part of the Future Generation Information Technology Conference, FGIT 2010, Jeju Island, Korea, December 13-15, 2010. Proceedings; 01/2010
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ABSTRACT: Moving object detection techniques have been studied extensively for such purposes as video content analysis as well as for remote surveillance. Video surveillance systems rely on the ability to detect moving objects in the video stream which is a relevant information extraction step in a wide range of computer vision applications. There are many ways to track the moving object. Most of them use the frame differences to analyze the moving object and obtain object boundary. This may be quite resource hungry in the sense that such approaches require a large space and a lot of time for processing. This paper proposes a new method for moving object detection from video sequences by performing frame-boundary tracking and active-window processing leading to improved performance with respect to computation time and amount of memory requirements. A stationary camera with static background is assumed. KeywordsMoving Object Detection–Frame Differencing–Boundary Tracking–Bounding Box–Active Window12/2010: pages 127-136;
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