Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

• 作 者: Pei-Xin Liu Zhao-Sheng Zhu Xiao-Feng Ye Xiao-Feng Li
• 机 构: School of Information and Communication Engineering, University ofElectronic Science and Technology
• 出 处: Journal of Electronic Science and Technology
• 发表日期: 2020年04期
• 关键词: Conditional random field (CRF) long short term memory network (LSTM) motion esti

In dense pedestrian tracking, frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories. In this study, a conditional random field tracking model is established by using a visual long