  In order to track the moving objects in long range against occlusion,
interruption, and background clutter, this paper proposes a unified approach
for global trajectory analysis. Instead of the traditional frame-by-frame
tracking, our method recovers target trajectories based on a short sequence of
video frames, e.g. $15$ frames. We initially calculate a foreground map at each
frame, as obtained from a state-of-the-art background model. An attribute graph
is then extracted from the foreground map, where the graph vertices are image
primitives represented by the composite features. With this graph
representation, we pose trajectory analysis as a joint task of spatial graph
partitioning and temporal graph matching. The task can be formulated by
maximizing a posteriori under the Bayesian framework, in which we integrate the
spatio-temporal contexts and the appearance models. The probabilistic inference
is achieved by a data-driven Markov Chain Monte Carlo (MCMC) algorithm. Given a
peroid of observed frames, the algorithm simulates a ergodic and aperiodic
Markov Chain, and it visits a sequence of solution states in the joint space of
spatial graph partitioning and temporal graph matching. In the experiments, our
method is tested on several challenging videos from the public datasets of
visual surveillance, and it outperforms the state-of-the-art methods.
