Abstract
The existing real-time pedestrian detection method often loses part of the detection accuracy. For this reason, we proposed a real-time pedestrian detection algorithm based on tiny-yolov3. The proposed method uses K-means clustering on our training set to find the best priors. We improved the network structure of tiny-yolov3 to make it more accurate in pedestrian detection. From the experimental results, the proposed method has higher detection accuracy under the premise of satisfying real-time performance.
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