Brain-inspired long-time-interval continuous pedestrian re-identification method and device

The invention discloses a brain-inspired long-term continuous pedestrian re-recognition method and device, and the method comprises the steps: training a deep neural network through employing initial training data, and obtaining an initial pedestrian re-recognition model; extracting scene memory buffer data in the initial training data, and obtaining preset parameters in the initial pedestrian re-recognition model; when the initial pedestrian re-identification model is retrained according to the newly added training data and the scene memory buffer data, preset parameters are constrained, so that the preset parameters meet constraint conditions; and calculating a loss function of the retrained initial pedestrian re-identification model, and performing iterative updating on the scene memory buffer data and preset parameters according to the loss function until the current pedestrian re-identification model meets an iteration termination condition, thereby obtaining a final pedestrian re-identification model. A