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Real-time pedestrian detection and tracking are vital to many applications, such as the interaction between drones and human. However, the high complexity of Convolutional Neural Network (CNN) makes them rely on powerful servers, thus is hard for mobile platforms like drones. In this paper, we propose a CNN-based real-time pedestrian detection and tracking system, which can achieve 14.7 fps detection...
Convolutional Neural Network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many applications. However, for embedded platforms, CNN-based solutions are still too complex to be applied if only CPU is utilized for computation. Various dedicated hardware designs on FPGA and ASIC have been carried out to accelerate CNN, while few of them explore...
Deep learning, and especially Convolutional Neural Network (CNN, is among the most powerful and widely used techniques in computer vision. Applications range from image classification to object detection, segmentation, Optical Character Recognition (OCR), etc. At the same time, CNNs are both computationally intensive and memory intensive, making them difficult to be deployed on low power lightweight...
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