Pedestrian re-identification method based on triple convolutional neural network

Abstract

The invention discloses a pedestrian re-identification method based on a triple convolutional neural network. The method comprises the steps of 1, forming a small batch of samples; 2, outputting global features and local features of each pedestrian image; 3, calculating the distance between the global features of every two pedestrian images in the small-batch samples and the distance between the local features of the corresponding body parts; 4, forming P x K pairs of triple samples; 5, using a Log-logistic function to replace a hinge function to improve a triple convolutional neural network loss function; 6, obtaining a trained triple convolutional neural network; asnd step 7, identifying the target pedestrian image by using the trained triple convolutional neural network, and further avoiding mismatching between the target pedestrian image and the pedestrian image to be identified by combining a confidence measurement mechanism. According to the pedestrian re-identification method based on the triple convolutional neural network, pedestrian re-identification can be accurately carried out, and mismatching between different pedestrians is avoided.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a pedestrian re-identification method based on a triple convolutional neural network. Background technique [0002] Person re-identification is one of the important fields of image processing and pattern recognition research. The so-called pedestrian re-identification refers to: use a specific searched pedestrian as the search source, and automatically find this search source in the video images collected by other cameras without repeated sight. When a situation occurs, the target object must move between multiple cameras and different scenes. How to identify the same person appearing under different cameras and draw the path that the target walked is the focus of pedestrian re-identification research. [0003] Although pedestrian re-identification technology has broad application prospects in video control, intelligent transportation, multimedia retrieval an...

AI Technical Summary

Problems solved by technology

However, if this method is directly applied to pedestrian re-identification, the following two disadvantages will occur: 1) The interval parameters in the traditional triplet loss function need to be specified in advance, and the value range in the process of adaptive feature learning cannot be adjusted during training. 2) There are many similar samples in the data set, and it often happens that the negative samples match the test image better than the positive samples, and there will be errors for different pedestrians with similar appearance characteristics. matching phenomenon

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