In this paper, a Histogram of Oriented Gradients (HOG) feature extraction accelerator for real-time multiple object detection is presented. The processor employs three techniques: a VLSI-oriented HOG algorithm with early classification in Support Vector Machine (SVM) classification, a dual core architecture for parallel feature extraction, and a detection-window-size scalable architecture with a reconfigurable MAC array for processing objects of different shapes. Early classification reduces the number of computations in SVM classification. The dual core architecture and the detection-window-size scalable architecture enable the processor to operate in several modes: high-speed mode, low-power mode, multiple object detection mode, and multiple shape object detection mode. These techniques expand the processor flexibility required for versatile application. The test chip was fabricated using 65 nm CMOS technology. The proposed architecture is designed to process HDTV resolution video (1920 × 1080 pixels) at 30 frames per second (fps). The performance of this accelerator is demonstrated on a pedestrian detection system.
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