Video Object Detection and Segmentation Based on Proposal Boxes

Abstract

In this paper, we propose a new method to detect and segment foreground object in video automatically. Given a video sequence, our method begins by generating proposal bounding boxes in each frame, according to both static and motion cues. The boxes are used to detect the primary object in the sequence. We measure each box with its likelihood of containing a foreground object, connect boxes in adjacent frames and calculate the similarity between them. A layered Directed Acyclic Graph is constructed to select object box in each frame. With the help of the object boxes, we model the motion and appearance of the object. Motion cues and appearance cues are combined into an energy minimization framework to obtain the coherent foreground object segmentation in the whole video. Our method reports comparable results with state-of-the-art works on challenging benchmark dataset.