Abstract
Vehicle detection methods are playing an important role for driver assistance systems. Developing a high accuracy and efficiency vehicle detection system thus becomes crucial. One of the popular approaches is the scanning method which is based on the sliding window search for locating the vehicles from the input images. Such method provides a high detection rate with a time consuming process that identifies the vehicle from each sliding window. The searching time can be unacceptable as the searching space grows. This raises an optimization opportunity to exploit modern heterogeneous multicore system to accelerate the vehicle detection process. In this paper, we present a case study to accelerate a sliding-window based vehicle detection algorithm on a heterogeneous multicore systems using OpenCL designs. Unlike transitional detection algorithm, we integrate width model into our vehicle detection method to reduce search space. We give a detail execution profiling on each component of original vehicle detection algorithm and explore the potential parallelism. The experiment is based on a heterogeneous multicore platform that includes an Intel i5-2400 processor and a AMD HD6670 GPU. Also an Open64-based OpenCL compiler is employed to compile the cl code for the GPU. Significant performance speed-up is achieved with our parallelization and optimization, the maximum speed-up for the vehicle detection kernel and whole application is 17.1 and 16.7 respectively. © 2013 IEEE.