Parallel Object Detection on Multicore Platforms

Abstract

Object detection is an important function for intelligent multimedia processing, but its computational complexity prevented its pervasive uses in consumer electronics. Cost-effective & energy-efficient computations are now available with various innovative multicore architectures proposed for embedded systems. However, extensive software optimizations are needed to unravel the inherent parallelisms in object detection for multicore processing. This paper presents interleaved reordering and splitting of parallel tasks in object detection. Overall performance improvements by 10% & 19% have been measured for the proposed methods respectively on a face detection prototype implemented on Sony PlayStation 3.