A Spatio Temporal Texture Saliency Approach for Object Detection in Videos

Detecting what attracts human attention is one of the vital tasks for visual processing. Saliency detection finds out the location of foci of attention on an outstanding object in image/video sequences. However, temporal information in videos play major role in human visual perception in locating salient objects. This paper presents a novel approach to detect salient object in a video using spatio-temporal textural saliency which also includes temporal information, an important aspect in videos. In this work, the context driven static saliency extracted from Lab color space in XY plane is combined with the local phase quantization on three orthogonal planes (LPQ-TOP) driven dynamic saliency to detect the spatio-temporal saliency in videos. The dynamic saliency is obtained by fusing two temporal saliencies extracted from XT-plane and YT-plane using LPQ texture feature, which extracts the temporal salient region. This approach is evaluated on Benchmark dataset and the result shows that the proposed saliency approach yields promising performance.