The invention discloses a
pedestrian searching method and device based on structural
perception self-attention and online instance aggregation matching, and belongs to the technical field of computervision technology
processing. The method comprises the following steps: firstly, in a
training phase, combining a
convolutional neural network with a non-local layer; carrying out
feature extraction on an input whole scene image to obtain feature representation of the scene image, designing structure-perceived anchor points for a special object of a
pedestrian, improving the performance of a detection framework, framing the detected
pedestrian into the same size, then sending the pedestrian into a pedestrian re-identification network, and carrying out training, storage, optimization and updating of pedestrian features with tags. In the
model testing stage, the trained non-local
convolutional neural network is used for carrying out
pedestrian detection on an input scene image, and after a pedestrian frame is detected, a target pedestrian image is used for carrying out special
similarity matching sorting and retrieval.
Pedestrian detection and re-identification can be carried out on large-scale real scene images at the same time, and the method plays an important role in the security and protection fields of urban monitoring and the like.