Abstract:
This paper presents a fast pedestrian and vehicle detection framework that integrates GPU (graphics processing unit) and CPU implementations. We employ the Histograms of Oriented Gradients (HoG) and the Feature Interaction Descriptor (FIND) as object descriptors. FIND describes the high-level properties of an object's appearance by computing pair-wise interactions of adjacent region-level features. We also employ the cascade approach in a sliding-window manner with multi-classifiers specialized for both the direction of a pedestrian and the distance of the pedestrian to a camera installed on a vehicle. Although our detection framework can detect pedestrians and vehicles in images, it wastes computational cost on calculating high-dimensional FIND features. Therefore, to realize real-time processing, we utilize the NVIDIA CUDA framework for pedestrian and vehicle detection. Three parallelization techniques implemented on both a GPU and a CPU are incorporated in our detection framework. As a result, our proposed implementation can perform detection more than 30 times faster than can a conventional implementation running on a CPU. For a 640×480 image, our parallel techniques attain a processing speed of 23.8 fps (42 [ms]) and detects both pedestrians and vehicles.

Paper available at IEEE.