Optimized Parallel Model of Covariance Based Person Detection

Covariance descriptor has good performance for person detection systems. However, it has high execution time. Multiprocessors systems are usually adopted to speed up the execution of these systems. In this paper, an optimized parallel model for covariance person detection is implemented using a high-level parallelization procedure. The main characteristics of this procedure are the use of Khan Process Network (KPN) parallel programming model of computation, and the exploration of both task and data levels of parallelism. For this aim, a first KPN parallel model is proposed starting from the block diagram of the covariance person detection application. This model is implemented through the Y-Chart Application Programmers Interface (YAPI) C++ library. To ensure the best workload balance of the optimized model, communication and computation workload analysis are considered. Based on these results, both task merging and data-level partitioning are explored to derive an optimized model with the best communication and computation workload balance. The optimized parallel model obtained has three times lower execution time in comparison with the sequential model.