Research And Implementation Of Pedestrian Detection And Re-Identification Technology For Complex Scenes

Pedestrian detection and re-identification work is a research hotspot in the field of computer vision.It aims at studying the detection of pedestrians and the matching of specific pedestrians within multiple non-overlapping camera areas,and is widely used for intelligent city’s urban construction to ensure travel safety.However,there exists problems of pose,occlusion,lighting,perspective,background,resolution and other open ones for the data obtained in complex environments.This thesis will focus on the above-mentioned challenges and difficulties to carry out the research work,so as to promote the wide applications of pedestrian detection and re-identification technology in intelligent cities,and support for the construction of “safe city”.Based on the deep learning technology,this thesis focuses on the research of pedestrian detection and re-identification.The main work includes the following three aspects:(1)A pedestrian detection model,named Bi-YOLOv5 with bidirectional-feature fusion network is proposed.Firstly,a bidirectional-feature fusion network with CBAM module is used to enhance the ability to extract effective pedestrian features.Secondly,the EIo U Loss is applied to accelerate the speed of the model convergence.Finally,a detection post-processing method,DIo U-NMS,is employed to remove redundancy of anchor box.Through comparative experiments on two datasets,it proves that the proposed Bi-YOLOv5 model has better compatibility in complex environments.(2)A pedestrian re-identification model,named HOFRe-ID,with feature fusion of highorder topological key points is developed.Firstly,the methods of GCN is used to model key point features to acquire high-order relational information.Secondly,global features are infused in the graph matching phase to acquire better descriptive factors features.Finally,the Batch Hard Triplet Loss is applied to guide network training to improve the model’s ability to mine difficult samples.It shows from the experimental results that the proposed HOFRe-ID model has better robustness and effectiveness in solving pedestrian occlusion.(3)A pedestrian detection and re-identification system is established.The system is based on the proposed Bi-YOLOv5 and HOFRe-ID models and is built by Qt framework.It does not restrict the limitation of data acquisition way,and could realize the detection and reidentification of pedestrian data in multiple ways.In the meantime,the deep learning method is used to reduce the cost of pedestrian detection and re-identification work,and further verify the effectiveness and practicability of the proposed algorithms.