关键词: 跨镜行人追踪, 深度学习, 迁移学习, 三元组损失

Abstract:

Person re-identification is an important topic in the fields of computer vision and public safety systems based on video surveillance. With the development of large-scale data sets and extensive research on deep learning networks, deep learning has achieved good results in person re-identification problems. However, in addition to different visual appearance changes caused by different cameras and different perspectives of the video itself, the overall data set for person re-identification is small, and the number of labeled training data samples is smaller, which limit the person re-identification effect of deep learning. An improved deep transfer learning algorithm for person re-identification is proposed in this paper, which migrates mature deep learning models that have been trained on large data sets to the target data set for fine-tuning, and combines the target data to optimize the model to make it better feature extraction for a new data set. During the model training process, by improving the triplet loss function, the distance between the same samples is closer, and the distance between different samples is farther. At the same time, the distance between positive samples is set to be not greater than a threshold, which ensures that the clusters formed in the feature space will not be too large. The algorithm in this paper reduces the time for training deep learning models, avoids the shortcomings of insufficient data on small data sets, and improves the accuracy of person re-identification. Comparative experiments on five standard data sets show that the proposed algorithm has achieved good person re-identification results.

Key words: person re-identification, deep learning, transfer learning, triplet loss