We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for candidate generation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance watt ratio than desktop CUDA platforms in study.
Since the appearance of GPGPU computational platforms, several object detection algorithms have been ported to the GPU. There are also examples of using Field Programmable Gate Array (FPGA) designs, obtaining outstanding results @cite_6 . In comparison, the reduced development costs of the CUDA programming environment and the affordable desktop CUDA enabled GPU cards make them more suitable for testing new algorithms.
@cite_6
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This paper focuses on real-time pedestrian detection on Field Programmable Gate Arrays (FPGAs) using the Histograms of Oriented Gradients (HOG) descriptor in combination with a Support Vector Machine (SVM) for classification as a basic method. We propose to process image data at twice the pixel frequency and to normalize blocks with the L1-Sqrt-norm resulting in an efficient resource utilization. This implementation allows for parallel computation of different scales. Combined with a time-multiplex approach we increase multiscale capabilities beyond resource limitations. We are able to process 64 high resolution images (1920 × 1080 pixels) per second at 18 scales with a latency of less than 150 u s. 1.79 million HOG descriptors and their SVM classifications can be calculated per second and per scale, which outperforms current FPGA implementations by a factor of 4.