A Pedestrian Re-Identification Method Based on Retinex Algorithm and Convolutional Neural Network

Abstract

The invention discloses a pedestrian re-identification method based on the retinex algorithm and convolutional neural network. Firstly, the sequence of video frames in the video database is extracted, the convolutional neural network is constructed and the pedestrian network model is trained, and the trained network model is used to identify pedestrians. Detected from the video frame sequence, the detected pedestrians are image-enhanced with the retinex algorithm, and finally the enhanced pedestrians are input into the convolutional neural network to extract the depth features of different levels of pedestrians, and through the softmax of the last layer of the convolutional neural network The classifier performs classification to obtain the final matching similarity. The invention fully considers the problems of illumination changes and shadow coverage in real scenes, introduces a retinex enhancement algorithm before recognition, imitates the human visual system, makes the image closer to what the human eye sees, and effectively improves the recognition effect. Using an end-to-end person re-identification method, the pedestrian detection and recognition are combined with the same convolutional neural network, and the alignment problem of pedestrian labels is solved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a pedestrian re-identification method based on a retinex algorithm and a convolutional neural network. Background technique [0002] Pedestrian re-identification is a hot research topic in the field of computer vision in recent years. Traditional pedestrian re-identification separates pedestrian detection and pedestrian recognition. First, the DPM or ACF algorithm is used to detect pedestrians, and the detected pedestrian bounding boxes are cropped for later use. Pedestrian recognition, this method is difficult to use in real scenes, and it is difficult to achieve complete alignment between the pedestrian detection area and the pedestrian recognition area, resulting in low pedestrian recognition. [0003] A popular method for pedestrian recognition is to use convolutional neural network to extract the picture features of pedestrian pairs for feature matchi...

AI Technical Summary

Problems solved by technology

The traditional neural network has a strong nonlinear fitting ability, can map any complex nonlinear relationship, has strong robustness and memory ability, and uses the low-level features extracted by the neural network, such as color texture empty structure features, etc., These features are easy to calculate and relatively reliable, but these underlying features do not have good semantic expression ability and are not closely related to the appearance of pedestrians

When there are changes in illumination and pedestrian rotation angles, these features often cannot express the characteristics of pedestrians well, resulting in deviations in recognition

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