The recent emergence of multi-core processors enables a new trend in the usage of computers. Computer vision applications, which require heavy computation and lots of bandwidth, usually cannot run in real-time. Recent multicore processors can potentially serve the needs of such workloads. In addition, more advanced algorithms can be developed utilizing the new computation paradigm. In this paper, we study the performance of an articulated body tracker on multi-core processors. The articulated body tracking workload encapsulates most of the important aspects of a computer vision workload. It takes multiple camera inputs of a scene with a single human object, extracts useful features, and performs statistical inference to find the body pose. We show the importance of properly parallelizing the workload in order to achieve great performance: speedups of 26 on 32 cores. We conclude that: (1) data-domain parallelization is better than functiondomain parallelization for computer vision appl...