Multiple Object Tracking Algorithm Based on Deep Learning
In this paper, a multi-target tracking algorithm based on deep learning is proposed. Firstly, GoogLeNet + long short-term memory (LSTM) model is used to obtain accurate object detection results. Secondly, the feature map of object detection is directly used to extract the deep feature for tracking. Compared with the traditional feature, the deep feature can reflect the appearance of objects more accurately, which could improve the tracking accuracy effectively. What's more, based on the traditional Data Driven Markov Chain Monte Carlo (DDMCMC) algorithm, the Hierarchical Data Driven Markov Chain Monte Carlo (HDDMCMC) algorithm is proposed to further improve the tracking accuracy. The experiment results prove the effectiveness of our algorithm.
NETL
NSTL
万方数据
维普
