Saliency/non-saliency segregation in video sequences using perception-based local ternary pattern features

The detection of salient objects in video sequence is an active research area of computer vision. One approach is to perform joint segmentation of objects and background in each image frame of the video. The background scene is learned and modeled. Each pixel is classified as background if it matches the background model. Otherwise the pixel belongs to a salient object. The segregation method faces many difficulties when the video sequence is captured under various dynamic circumstances. To tackle these challenges, we propose a novel perception-based local ternary pattern for background modeling. The local pattern is fast to compute and is insensitive to random noise, scale transform of intensity. The pattern feature is also invariant to rotational transform. We also propose a novel scheme for matching a pixel with the background model within a spatio-temporal domain. Furthermore, we devise two feedback mechanisms for maintaining the quality of the result over a long video. First, the background model is updated immediately based on the background subtraction result. Second, the detected object is enhanced by adjustment of the segmentation conditions in proximity via a propagation scheme. We compare our method with state-of-the-art background/foreground segregation algorithms using various video datasets.