Abstract
Object detection plays an important role in intelligent video analysis. Unfortunately, its heavy computational complexity makes it very difficult to process in real time. Some recent studies use multi-core platforms to achieve the required performance. In this paper, we study the problem under the context of many-core platforms, e.g. for application-specific, embedded systems. We first show how object detection can be parallelized for many-core platforms and then discuss how its performance can be predicted for embedded system designs. The parallel algorithm is verified with a real implementation on a 64-core TILERA. Our implementation achieves a speedup of 37.20 with 56 cores and a processing rate of 18 frames per second for full-HD (1920 * 1080) videos. Our performance prediction equation is also evaluated using the implementation and the predicted performance is very close to real results. © 2009 IEEE.