This paper describes a Histogram of Oriented Gradients (HOG) feature extraction accelerator that features a VLSI-oriented HOG algorithm with early classification in Support Vector Machine (SVM) classification, dual core architecture for parallel feature extraction and multiple object detection, and detection-window-size scalable architecture with reconfigurable MAC array for processing objects of several shapes. To achieve low-power consumption for mobile applications, early classification reduces the amount of computations in SVM classification efficiently with no accuracy degradation. The dual core architecture enables parallel feature extraction in one frame for high-speed or low-power computing and detection of multiple objects simultaneously with low power consumption by HOG feature sharing. Objects of several shapes, a vertically long object, a horizontally long object, and a square object, can be detected because of cooperation between the two cores. The proposed methods provide processing capability for HDTV resolution video (1920×1080 pixels) at 30 frames per second (fps). The test chip, which has been fabricated using 65nm CMOS technology, occupies 4.2×2.1mm² containing 502 Kgates and 1.22Mbit on-chip SRAMs. The simulated data show 99.5mW power consumption at 42.9MHz and 1.1V.