Pedestrian detection method based on deep learning and multi-feature point fusion
A technology of pedestrian detection and deep learning, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of mutual occlusion of pedestrians, and achieve the effect of improving accuracy and robustness
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[0020] A pedestrian detection method based on deep learning and multi-feature point fusion, including a training phase and a detection phase.
[0021] In the training phase, the pedestrian images in the application scene are first collected and the head and shoulders of the pedestrians in the images are marked, and then these pedestrian samples are used for model training. The model training is divided into two steps: 1) Use the head and shoulder images of pedestrians as training samples and use Triplet Loss to train a binary classification model of the head and shoulders of pedestrians; 2) use the model parameters obtained in step 1) to train Initialize some parameters of the pedestrian detection model by means of "migration learning". As the model used in the final detection stage, the pedestrian detection model adopts an end-to-end training method, including the functions of candidate area extraction, pedestrian feature extraction and feature classification.
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