Efficient Real-Time Object Detection via Parallel AdaBoost Architectures

Abstract

—Real-time object detection is becoming necessary for a wide number of applications related to computer vision and image processing, security, bioinformatics, and several other areas. Existing software implementations of object detection algorithms are constrained in small-sized images and rely on favorable conditions in the image frame to achieve real-time detection frame rates. Efforts to design hardware architectures have yielded encouraging results, yet are mostly directed towards a single application, targeting specific operating environments. Consequently, there is a need for hardware architectures capable of detecting several objects in large image frames, and which can be used under several object detection scenarios. In this work, we present a generic, flexible parallel architecture, which is suitable for all ranges of object detection applications and image sizes. The architecture implements the AdaBoost-based detection algorithm, which is considered one of the most efficient object detection algorithms. Through both FPGA emulation and largescale implementation, and RTL synthesis and simulation, we illustrate that the architecture can detect objects in large images (up to 1024x768 pixels) with frame rates that can vary between 64-139 fps for various applications and input image frame sizes