Study On Pedestrian Gait Recognition

In recent years,with the continuous development of artificial intelligence,intelligent security has been widely used in real life.It provides a guarantee for safe cities and ensures the safety of the people.Nowadays,surveillance cameras are installed in every important location in the city to prevent criminal activities.Relying on video surveillance,it is an important task to describe pedestrian trajectories and retrieve specific persons.Among them,gait recognition is one of the key technologies.Gait is an important and stable biometric recognition technology based on human walking posture and body shape.Therefore,the study of gait recognition has extremely high application value.Research on gait recognition based on deep learning has made great progress.The establishment of public gait datasets also provides a platform for algorithm evaluation.However,there are still some problems in recent research.First,gait images are collected indoors without any background interference and volunteers walk along a fixed path.Background subtraction and frame difference method are used to get pedestrian contour.However,the data and contours extraction methods in simulated scene are different from real scene,which makes a big gap between research and application.Secondly,recent gait recognition work mostly uses shallow neural networks for research.The shallow network can pay attention to local features and is not prone to overfitting.But its limited learning ability and lack of global features lead to low recognition performance.With the continuous improvement of computer vision network,how to use a deeper network to improve the accuracy of gait recognition has become an urgent task.Finally,there are difficulties in cross-view gait recognition.The contour and walking trajectory change significantly with the change of shooting angle.It causes a greater reduction in the accuracy of cross-view matching compared to the same-view matching.To solve the problems of gait recognition,this thesis does the following work:1.This thesis designs a gait contour extraction framework for monitoring scene and constructs a gait recognition dataset.It is robust to outdoor environments with complex environments and changing lighting conditions.The method includes detection module,tracking module and contour extraction module.To solve the low resolution of pedestrian in video image,this thesis increases the size of the feature map and adjusts the initialization parameters.A strong feature tracker is proposed for tracking module.Finally,combining the constour extraction module,a campus monitoring gait dataset is established to evaluate the gait recognition algorithm.It also builds a bridge between gait recognition research and application.2.This thesis proposes a multi-scale convolutional neural network for gait recognition.This thesis selects a better backbone,and merges the global and local features of the gait energy image to extract more discriminative features.Network combines Arcface loss function and Hard Triplet loss function for supervised learning.Under the supervision of multiple loss functions,we train an end-to-end gait recognition network.Experiments show that the Arcface loss function combined with Hard Triplet loss function can maximizes the network's ability to distinguish gait features.Meanwhile,the combination of global features and local features has stronger discrimination ability.3.This thesis implements a cross-view gait recognition gait recognition method based on generative adversarial networks.Among gait recognition tasks,cross-view recognition is the most challenging task.There are obvious differences in the images taken by the same pedestrian under different angles.This thesis designs a generative adversarial network structure and a triplet identity loss to convert the gait energy image from different angles into a fixed angle gait energy image while retaining pedestrian identity information.Experiments show that the features of the generated image can assists the original image to obtain higher accuracy recognition results.