A Hardware Architecture for Cell-Based Feature-Extraction and Classification Using Dual-Feature Space

作者：An, Fengwei; Zhang, Xiangyu; Luo, Aiwen; Chen, Lei; Mattausch, Hans Jurgen

来源：IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(10): 3086-3098.

DOI：10.1109/TCSVT.2017.2726564

• Cell-based
• Dual feature
• Feature extraction and classification
• Haar-like
• Histogram of oriented gradients (HOG)
• Machine learning applications
• Pipelined architecture
• Sliding window schemes

Many computer-vision and machine-learning applications in robotics, mobile, wearable devices, and automotive domains are constrained by their real-time performance requirements. This paper reports a dual-feature-based object recognition coprocessor that exploits both histogram of oriented gradient (HOG) and Haar-like descriptors with a cell-based parallel sliding-window recognition mechanism. The feature extraction circuitry for HOG and Haar-like descriptors is implemented by a pixel-based pipelined architecture, which synchronizes to the pixel frequency from the image sensor. After extracting each cell feature vector, a cell-based sliding window scheme enables parallelized recognition for all windows, which contain this cell. The nearest neighbor search classifier is, respectively, applied to the HOG and Haar-like feature space. The complementary aspects of the two feature domains enable a hardware-friendly implementation of the binary classification for pedestrian detection with improved accuracy. A proof-of-concept prototype chip fabricated in a 65-nm SOI CMOS, having thin gate oxide and buried oxide layers (SOTB CMOS), with 3.22-mm2 core area achieves an energy efficiency of 1.52 nJ/pixel and a processing speed of 30 fps for 1024 ×1616-pixel image frames at 200-MHz recognition working frequency and 1-V supply voltage. Furthermore, multiple chips can implement image scaling, since the designed chip has image-size flexibility attributable to the pixel-based architecture.

• 出版日期2018-10