Parallel Implementation and Performance Prediction of Object Detection in Videos on the Tilera Many-Core Systems

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.