A Study on Segmentation of Moving Objects Under Dynamic Conditions
Keywords:
Image Processing, Segmentation, Histogram, Moving Object Detection, Markov Random Field Information Saliency MapAbstract
One of the challenging factor in computer vision is Moving Object Detection under Dynamic condition, dynamic condition involves changes in the background like illumination changes, shadows, slowly moving background and the object, occlusion, noise in the video or image, motion of the camera. In order to overcome the problems of dynamic back ground, and detect the moving object correctly many algorithms have been proposed in the literature survey. In this paper an attempt has been made to study two algorithms for segmenting the moving objects from a video. Firstly the color and motion cues based segmentation is performed. In this method frames are extracted from the video and the motion information is considered and the color information is extracted using the color histogram method. The color and the motion information is combined using Markov Random Field (MRF) to segment the object from the back ground. The second algorithm is based on Spatio-Temporal method of segmentation. In this method spatial and temporal information of the frames are extracted separately. These features are combined to form Information Saliency Map(ISM) and from ISM the foreground is segmented from the back ground. The comparative study is performed on both the algorithms for segmentation. Analysis is performed on these methods and a little variation is found.
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