Adaptive Rao-Blackwellized particle filter and its evaluation for tracking in surveillance.
Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter.
Xu
Xinyu
X
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA. xinyu.xu@asu.edu
Li
Baoxin
B
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012637:Security Measures; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*