Dense Flow-Based Video Object Segmentation in Dynamic Scenario

Segmenting object from a moving camera is a challenging task due to varying background. When camera and object both are moving, then object segmentation becomes more difficult and challenging in video segmentation. In this paper, we introduce an efficient approach to segment object in moving camera scenario. In this work, first step is to stabilize the consecutive frame changes by the global camera motion and then to model the background, non-panoramic background modeling technique is used. For moving pixel identification of object, a motion-based approach is used to resolve the problem of wrong classification of motionless background pixel as foreground pixel. Motion vector has been constructed using dense flow to detect moving pixels. The quantitative performance of the proposed method has been calculated and compared with the other state-of-the-art methods using four measures, such as average difference (AD), structural content (SC), Jaccard coefficients (JC), and mean squared error (MSE).