We present the theory behind a novel unsupervised method for discovering quasi-static objects, objects that are stationary during some interval of observation, within image sequen...
Brandon C. S. Sanders, Randal C. Nelson, Rahul Suk...
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
Abstract. We describe progress in the automatic detection and identification of humans in video, given a minimal number of labelled faces as training data. This is an extremely cha...
This paper presents an approach to extracting and using semantic layers from low altitude aerial videos for scene understanding and object tracking. The input video is captured by...
Jiangjian Xiao, Hui Cheng, Feng Han, Harpreet S. S...
Robust real-time tracking of non-rigid objects in a dynamic environment is a challenging task. Among various cues in tracking, color can provide an efficient visual cue for this t...