In this paper, we present a framework for a robotic system with the ability to perform real-world manipulation tasks. The complexity of such tasks determines the precision and fre...
Danica Kragic, Lars Petersson, Henrik I. Christens...
Abstract. This paper presents a novel probabilistic approach to integrating multiple cues in visual tracking. We perform tracking in different cues by interacting processes. Each p...
Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning...
Chunhua Shen, Anton van den Hengel, Michael J. Bro...
A new method for object tracking in video sequences is presented. This method exploits the benefits of particle filters to tackle the multimodal distributions emerging from clutte...
Alexandros Makris, Dimitrios I. Kosmopoulos, Stavr...
We propose a kernel-density based scheme that incorporates the object colors with their spatial relevance to track the object in a video sequence. The object is modeled by the col...