In order to solve the problem of continuity and accuracy of regional pedestrian identification, a method for regional identification and tracking of specific pedestrians with fusion of face information is proposed by using the front and rear frame information of the video stream. The FaceNet face identity network is constructed using the lightweight MobilenetV3-Large feature extraction network, and a fully connected layer is added for classification. Cross-Entropy Loss and Triplet Loss are integrated to build a classifier for stable loss convergence. The identity recognition method is introduced into DeepSORT, and an identity constraint module is added to solve the IDswitch caused by changes in appearance or pedestrian direction. This method combines the two modules of identification network and pedestrian tracking, and then realizes the continuous identification and tracking of pedestrians in the area. The experimental results show that: in terms of identity recognition, the accuracy of the improved system is 11% higher than that of the original algorithm in the autonomous test link; the accuracy of the tracking model is 69.S3% in MOTA on the MOT16 dataset, and the improved system is compared with the original algorithm in the autonomous test. During the test, MOTA increased from 72.38% to 89.84%, while IDswitch was greatly reduced. It realizes the detection and continuous tracking of specific pedestrians in the area, and provides technical support for the behavior of pedestrians in the area.
Improved DeepSORT-based pedestrian tracking and recognition method with fused face information
2023-08-04
1372556 byte
Conference paper
Electronic Resource
English