This paper proposes a novel visualization approach, which can depict the variations between different human motion data. This is achieved by representing the time dimension of eac...
In this paper we present a new scheme for detection and tracking of specific objects in a knowledge-based framework. The scheme uses a supervised learning method: Support Vector M...
Lionel Carminati, Jenny Benois-Pineau, Christian J...
We propose statistical data association techniques for visual tracking of enormously large numbers of objects. We do not assume any prior knowledge about the numbers involved, and...
Margrit Betke, Diane E. Hirsh, Angshuman Bagchi, N...
Abstract. We propose a novel tracking algorithm based on the WangLandau Monte Carlo sampling method which efficiently deals with the abrupt motions. Abrupt motions could cause conv...
Tracking multiple interacting objects represents a challenging area in computer vision. The tracking problem in general can be formulated as the task of recovering the spatio-temp...