Real-Time Image Resizing Hardware Accelerator for Object Detection Algorithms

This paper describes motivation and hardware architecture for resizing input image frames from the camera in order to support real-time scale-invariant object recognition. Conventional implementation of object detection algorithms such as histogram of oriented gradients (HOG) based feature extraction, face detection using Haar classifiers often perform image resizing during the object recognition process. Our investigation reveals that this incurs significant performance overhead due to frequent memory accesses that are required for image resizing. This has motivated our approach to perform online resizing - simultaneously resizing of the input image when it is loaded into frame buffer memory - prior to the object recognition process. We propose a hardware architecture to accelerate image resizing and describe a hybrid processor-accelerator platform to generate different sizes of an image in real-time for object recognition.