Floating point HOG implementation for real-time multiple object detection

Object detection and localization in a video stream is an important requirement for almost all vision systems. In the article a design embedded into a reconfigurable device which is using the Histogram of Oriented Gradients for feature extraction and SVM classification for detecting multiple objects is presented. Superior accuracy is achieved by making all computations using single precision 32-bit floating point values in all stages of image processing. The resulting implementation is fully pipelined and there is no need for external memory. Finally a working system able to detect and localize three different classes of objects in color images with resolution 640×480 @ 60fps is presented with a computational performance above 9 GFLOPS.