We present a Dynamic Data Driven Application System (DDDAS) to track 2D shapes across large pose variations by learning non-linear shape manifold as overlapping, piecewise linear s...
Most existing subspace analysis-based tracking algorithms utilize a flattened vector to represent a target, resulting in a high dimensional data learning problem. Recently, subspa...
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, ...
We propose a novel approach to designing algorithms for
object tracking based on fusing multiple observation models.
As the space of possible observation models is too large
for...
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deli...
Bernhard Zeisl, Christian Leistner, Amir Saffari, ...
We present new results from a real-user evaluation of a data-driven approach to learning user-adaptive referring expression generation (REG) policies for spoken dialogue systems. ...