PEDESTRIAN RE-IDENTIFICATION METHOD BASED ON SPATIO-TEMPORAL JOINT MODEL OF RESIDUAL ATTENTION MECHANISM AND DEVICE THEREOF

The disclosure provides a pedestrian re-identification method based on a spatio-temporal joint model of a residual attention mechanism and a device thereof. The method includes: performing feature extraction for an input pedestrian with a pre-trained ResNet-50 model; constructing a residual attention mechanism network including a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer; calculating a feature distance by using a cosine distance and denoting the feature distance as a visual probability according to the trained residual attention mechanism network; performing modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample, and performing Laplace smoothing for a probability model; and calculating a final spatio-temporal joint probability by using the visual probability and the spatio-temporal probability to obtain a pedestrian re-identification result.