A pedestrian re-identification method based on deep multi-view feature distance learning

Abstract

The pedestrian re-identification method based on deep multi-view feature distance learning is specifically implemented according to the following steps: step 1, extracting region feature vectors; Step2, region division: according to all the feature vectors of the image obtained in the step 1, carrying out normalization through a normalization algorithm l2 norm; Representing a vector set of the image in a summation mode, and performing l2 norm normalization processing on image representation; dividing One image into N regions, and obtaining depth region aggregation characteristics; Step 3, LOMO feature extraction: respectively extracting traditional LOMO features from pedestrian images in the reference set and the test set; 4, carrying out multi-view feature distance learning, and obtaining two distances from the two aspects of depth region aggregation features and LOMO features through XQDA training of the two features; Step 5, a weighted fusion strategy: carrying out parameter weighted fusion on the two distances obtained in the step 4 to obtain a final distance, and obtaining a matched grade according to the final distance; The robustness of pedestrian re-identification can be obviously improved; And the pedestrian re-identification performance is improved.

Description

technical field [0001] The invention belongs to the technical field of image analysis and image recognition, and in particular relates to a pedestrian re-identification method for deep multi-view feature distance learning. Background technique [0002] In recent years, the demand for surveillance camera networks in public security, commercial activities, smart transportation, national defense, and military applications has increased, such as: installing surveillance camera networks in airports, subways, stations, banks, schools, and military facilities. It is based on automatic unmanned monitoring for safety, so as to effectively ensure the safety of national facilities and the public. Just because the surveillance camera network has such a great application prospect, the intelligent video surveillance system has attracted the attention of many countries, and invested a lot of money to carry out extensive research work. [0003] The problem of person re-identification is th...

AI Technical Summary

Problems solved by technology

Since features from higher layers have large receptive fields and are easily polluted by human poses and background clutter, they cannot be adequately applied to person re-identification

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