Pedestrian re-identification method based on CNN and convolutional LSTM network
A pedestrian re-recognition and network technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of difficult appearance features, inability to learn features, and the appearance relationship of pedestrians is not close, and achieve the effect of close relationship
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[0029] The method scheme of the present invention: Given a series of continuous pedestrian images in videos, first use the frame-level convolutional layer in CNN to extract the CNN features to capture complex changes in appearance, and then send the extracted features to the convolution In the LSTM encoding-decoding framework, the encoding framework uses a local adaptive core to capture the actions of pedestrians in a sequence, thereby encoding the input sequence into a hidden representation, and then using a decoder to decode the hidden representation output by the encoding framework into a sequence. After LSTM encoding and decoding, a frame-level depth spatiotemporal appearance descriptor is obtained. Finally, Fisher vector coding is used to enable the descriptor to describe video-level features.
[0030] In order to make the pedestrian re-identification method based on CNN and convolutional LSTM network proposed in the present invention clearer, the following takes the use of ...
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