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International Journal of Spatio-Temporal Data Science

 

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International Journal of Spatio-Temporal Data Science (1 paper in press)

 

Regular Issues

 

  • Adaptive Background Modeling Technique for Moving Object Detection in Video under Dynamic Environment   Order a copy of this article
    by Dileep Yadav, Karan Singh 
    Abstract: This work proposes a novel method for detection of motion based object having dynamic scenario in the background. The suggested scheme has a strong potential for real-time applications especially for rafting, river, sea-beach, swimming pools, ponds, etc. Apart from these, this work is very beneficial for surveillance of border, tunnel, traffic in the sea, forest, restricted zones, deep zones, etc. This work develops a statistical p based background subtraction method and implemented in three stages. In the first stage, a background model is developed using few initial frames. In the second stage, this work classifies the foreground using the difference frame and the appropriate threshold value. An automatic threshold value is generated at run-time and updated iteratively. It also reduces the problem of using a constant threshold. In the third stage, morphological filters and connected component based region filtering technique is applied to enhance the detection quality. The extensive experimental result shows more accurate results of proposed method. It also demonstrates better performance against considered state-of-the-art methods.
    Keywords: Cluttered Background; Adaptive Modeling; Background Subtraction; Outliers; Moving Object Segmentation; Visual Surveillance.