Research On Pedestrian Detection And Tracking System Based On Deep Learning

Pedestrian tracking is an important research direction in the field of computational vision and pattern recognition.The specific process can be divided into pedestrian detection and identification of pedestrian identity,and finally,the tracking of pedestrians is given.In the whole process,due to the influence of pedestrian motion,occlusion and light,there are some errors in distinguishing pedestrians from the background.Pedestrian identification is the problem of pedestrian recognition,including feature extraction and feature similarity comparison,which has the problems of poor robustness and low accuracy.Therefore,pedestrian detection and tracking has a good research value.The main work of this thesis is as follows: 1.Pedestrian detection;2.pedestrian tracking.and the specific research is as follows:In the aspect of pedestrian detection,we mainly discuss the existing Cascade R-CNN network structure,including feature extraction network,region recommendation network,ROI pooling and the selection of activation function.Because the network itself is a Cascade R-CNN form of Faster R-CNN,this thesis designs its parameters,and improves the above network modules.The feature extraction module uses the feature form inserted into SE-network,and the area recommendation module uses the fixed anchor size to adapt to the fixed type of pedestrian target.In the final output,multiple scale features are fused,and ROI Align is used instead of ROI pooling.Finally,the data of the VOC format is made by using the data of the KITTI data set,and the model training is carried out and tested.In the aspect of pedestrian re-recognition,the first part is the review of pedestrian re-recognition research,and the second part is the structure of pedestrian re recognition network and pedestrian tracking model proposed in this thesis.The feature extraction module of the network uses Res Net50 residual neural network,and the feature fusion of the whole network uses the combination of two feature fusion methods.The first part is the direct extraction of the overall features of the image,Then,the whole feature is divided into8 parts of 2×4,the whole feature and the segmented part are downsampled,and the obtained part feature and the whole feature are fused into a long feature vector;the second part is to directly divide the image into three parts horizontally,the ratio from top to bottom is 2:7:7,and each part is extracted for feature extraction,and the extracted features are analyzed In the whole model,the features obtained in the first part and the features obtained in the second part are fused as the overall features,and the loss function uses the multi loss function strategy,combining the note smoothing loss function and the sorting table loss function.In the pedestrian re-recognition network,the pedestrian identity storage module is introduced to match and update the pedestrian information.The initialization and training of the network use Mark 1501 data set,which is verified in Mot16 data set.At the same time,the comparison results of different features introduced in this thesis are compared.Based on the existing tools,this thesis develops a pedestrian detection and tracking system.The system includes user login interface,sub function selection interface,which respectively realizes single target pedestrian detection,multi-target pedestrian detection,single target pedestrian tracking,multi-target pedestrian tracking,a total of four functions.And attached a partial screenshot of the function.