FPGA-accelerated Active Shape Model for real-time people tracking

Active Shape Model has been proven to be one of the most popular methods for recognizing non-rigid objects, which requires huge computation power for real time people tracking. After analyzing the parallel characteristics of the algorithm, we propose a deep pipelined structure for accelerating the Active Shape Model algorithm. The computing engine is organized into a deep pipeline network composing of multiple floating-point arithmetic units, including adders, multipliers, dividers and SQRT etc. A linear multiplication-accumulation (MAC) unit is designed to lower the complexity of the computing resources while keeping high pipeline throughput. In the optimization of the memory efficiency for loading random data in large images during the step of local search, we propose an on-chip buffer scheme to eliminate random accesses to off-chip memory. Experimental results show that our FPGA implementation achieves over 15 times of speedup compared with the sequentially-implemented software solution in Pentium 4 computer.