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An embedded hardware-efficient architecture for real-time cascade Support Vector Machine classification

Support Vector Machines (SVMs) are considered as a state-of-the-art classification algorithm yielding high accuracy rates. However, SVMs often require processing a large number of support vectors, making the classification process computationally demanding, especially when considering embedded applications. Cascade SVMs have been proposed in an attempt to speed-up classification times, but improved performance comes at a cost of additional hardware resources. Consequently, in this paper we propose an optimized architecture for cascaded SVM processing, along with a hardware reduction method in order to reduce the overheads from the implementation of additional stages in the cascade, leading to significant resource and power savings for embedded applications. The architecture was implemented on a Virtex 5 FPGA platform and evaluated using face detection as the target application on 640×480 resolution images. Additionally, it was compared against implementations of the same cascade processing architecture but without using the reduction method, and a single parallel SVM classifier. The proposed architecture achieves an average performance of 70 frames-per-second, demonstrating a speed-up of 5× over the single parallel SVM classifier. Furthermore, the hardware reduction method results in the utilization of 43% less hardware resources and a 20% reduction in power, with only 0.7% reduction in classification accuracy.