This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is repr...
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
— The Bayesian occupancy filter (BOF) [1] has achieved promising results in the object tracking applications. This paper presents a new development of BOF which inherits origina...
Cheng Chen, Christopher Tay, Christian Laugier, Ka...
We present an object detection and tracking algorithm that addresses the problem of multiple simultaneous targets tracking in realworld surveillance scenarios. The algorithm is bas...
We present a new algorithm that provides an efficient localization method of elliptic industrial objects. Our proposed feature extraction inherits edge grouping approaches. But ins...