We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by nonparametric...
We propose a unified framework for deriving and studying soft-in soft-out (SISO) detection in multiple-access channels using the concept of variational inference. The proposed fram...
The Continuously Adaptive Mean Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking that is intended as a step towards head and face trackin...
Tracking of humans in videos is important for many applications. A major source of difficulty in performing this task is due to inter-human or scene occlusion. We present an appr...