A deep residual network-based gender recognition method, comprising: obtaining a preset number of video frames of a target object from a video stream on the basis of a pedestrian tracking algorithm (S110); inputting the preset number of video frames into a pre-trained gender recognition model to obtain gender prediction values corresponding to the target object in the preset number of video frames, respectively, wherein the gender recognition model is pre-trained on the basis of a deep residual network (S120); weighting the gender prediction values to obtain the weighted gender prediction values of the target object (S130); and obtaining the gender recognition result of the target object according to the weighted gender prediction values (S140). The method can achieve real-time gender recognition of a pedestrian without face recognition, can achieve high gender recognition efficiency and accuracy, and meets the practical application needs of real-time pedestrian gender recognition.
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