基于深度学习与多级匹配机制的港区人员轨迹提取

Due to the complex spatial layout of the port environment, the difficulty of accurate tracking of port staff exists under the interference of complex backgrounds such as container yards, lifting machinery, loading, unloading, and transportation equipment. This study proposes a trajectory extraction framework based on a Faster-RCNN detection algorithm and an improved Deep SORT tracking algorithm for port surveillance video. In this framework, an adaptive Gaussian noise reduction and histogram equalization algorithm were added, and the image enhancement technology and Person Re-identification network were integrated to extract the feature information of port images, to improve the rapidity and accuracy of the track extraction of port staff. The detection results of the port staff image sequence were output through the pre-feature extraction network, the candidate region suggestion network, the pool of interest area, and the full connection layer. The location information of port staff was matched by cascade matching and the Hungarian algorithm. Finally, the motion trajectory of port staff was predicted by the Kalman filter. The results show that the proposed method has good performance in the face of challenges such as different light changes, low visibility, and shadow interference in each typical port scene. The average values of E_IDF1 , E_IDR , E_RCLL , and E_MOTA are 98%, 97%, 97%, and 95%, respectively. The conclusion shows that the FRIMDS framework proposed in this study has certain accuracy and stability, and can provide technical support for the safety supervision of automated terminals.