High performance architecture for object detection in streamed video (abstract only)

Abstract

Object detection is one of the key tasks in computer vision. It is computationally intensive and it is reasonable to accelerate it in hardware. The possible benefits of the acceleration are reduction of the computational load of the host computer system, increase of the overall performance of the applications, and reduction of the power consumption. We present novel architecture for multi-scale object detection in video streams. The architecture uses scanning window classifiers produced by WaldBoost learning algorithm, and simple image features. It employs small image buffer for data under processing, and on-the-fly scaling units to enable detection of object in multiple scales. The whole processing chain is pipelined and thus more image windows are processed in parallel. We implemented the engine in Spartan 6 FPGA and we show that it can process 640x480 pixel video streams at over 160 frames per second without the need of external memory. The design takes only a fraction of resources, compared to similar state of the art approaches.