We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 [8] challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking. We are motivated by the fact that the objects semantic category tends not to change throughout the video while its appearance and location can ...

As moving objects always draw more attention of human eyes, the temporal motion information is exploited complementarily with spatial to detect salient in videos. Although efficient tools such as optical flow have been proposed extract information, it often encounters difficulties when used for saliency detection due movement camera or partial objects. In this paper, we investigate complementar...

Automatic moving object detection is essential for various computer vision applications like video surveillance systems. Many previous moving object detection methods work for usually low-res video sequences under certain constraints. Either they perform detection process based on background learning and/or pixel-level motion analysis or they focus on detecting particular objects such as faces....

Smart surveillance system refers to video level processing techniques for identification of unwanted (terrorist) faces from real time video. Video object segmentation is an important part of real time surveillance system. For any video segmentation algorithm to be suitable in real time, must require less computational load. The work presented here is divided into two main parts: (1) Face Detect...