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This paper describes a Histogram of Oriented Gradients (HOG)-based object detection processor. It features a simplified HOG algorithm with cell-based scanning and simultaneous Support Vector Machine (SVM) calculation, cell-based pipeline architecture, and parallelized modules. To evaluate the effectiveness of our approach, the proposed architecture is implemented onto a FPGA prototyping board. Results show that the proposed architecture can generate HOG features and detect objects with 40 MHz for SVGA resolution video (800 × 600 pixels) at 72 frames per second (fps).

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 65 nm CMOS technology, occupies 4.2 × 2.1 mm 2 containing 502 Kgates and 1.22 Mbit on-chip SRAMs. The simulated data show 99.5 mW power consumption at 42.9 MHz and 1.1 V.