一种端到端的个体出行轨迹重识别的深度学习方法

对于行人的再识别研究大多采用图像处理和计算机视觉领域的相关方法,在社会治安领域和商业领域内受到了越来越多的关注.从信息检索的角度出发,提出了一种端到端的深度学习框架,对匿名化的基于位置的服务(LBS)数据进行用户再识别.首先,该框架采用嵌入网络对输入的位置序列及其对应的时间序列进行编码;然后采用递归循环网络对用户每天的...

An end-to-end deep learning method for individual travel path re-identification

A large number of researches on pedestrian re-identification based on the methods of image processing and computer vision were getting more and more attention in the field of social security and business. From the perspective of information retrieval,an end-to-end deep learning framework was proposed for user re-identification of anonymous location based services (LBS)data in this paper. Firstly, the embedded network was used to encode the input spatial sequence and the corresponding temporal sequence. Secondly,the recurrent network was adopted to encode the user''s daily history trajectory. Thirdly,the attention mechanism network was connected to calculate the importance weight of the two trajectories to be compared, and finally the similarity of the two trajectories was obtained. The experimental results showed that this model was able to take the user''s spatial-temporal position information into account, and achieve more accurate similarity between trajectory sequences compared with the traditional method of calculating the vector distance between trajectories. The re-identification accuracy of different number of users on the anonymous LBS dataset of a city was significantly improved.