Salient object detection in videos by optimal spatio-temporal path discovery

Many consumer videos focus on and follow salient objects in a scene. Detecting such salient objects is thus of great interests to video analytics and search. Instead of detecting salient object in individual frames separately, we propose to detect and track salient object simultaneously by finding a spatio-temporal path of the highest saliency density in the video. As salient video objects usually appear in consecutive frames, leveraging the motion coherence of videos can detect salient object more robustly. Without any prior knowledge of the salient objects, our method can automatically detect the salient objects of different shapes and sizes, and is able to handle noisy saliency maps and moving cameras. Experimental results on two public datasets demonstrate the effectiveness of the proposed method on salient video object detection.