Detecting people or other articulated objects and localizing their body parts
is a challenging computer vision problem as their movement is unpredictable
under circumstances of partial and full occlusions. In this paper, a framework
for human body parts tracking in video sequences using a self-adaptive clus-
ter background subtraction (CBS) scheme is proposed based on a Gaussian
mixture model (GMM) and foreground matching with rectangular pictorial
structures. The efficiency of the designed human body parts tracking frame-
work is illustrated over various real-world video sequences.