| Popis: |
In construction,communications,power and other engineering industries,workers need to perform outdoor operations frequently.Due to the complex outdoor environment,there are high risks from factors like high voltage,high altitude,and deep pits in many operation and maintenance tasks.Once an accident happens,huge losses in personnel and property would be caused.Therefore,it is necessary to verify identities of operators during the operation process for supervision.However,in the traditional supervision method,the personnel management and behavior control in the operation scenes rely entirely on manual verification,and the surveillance video also relies on manual guards.It is impossible to achieve real-time verification of personnel identities and effective warning of the entry of non operators.For the identification of workers in outdoor work scenes,most of the current research methods are based on the face recognition.Face recognition method can accurately identify the identity information of a worker when his facial information is clear and complete.However,when the facial information is incomplete or fuzzy because of occlusion,long detection distance or the inclined detection angle,it will be difficult to accurately identify the operator's identity with the face recognition method.Gait feature is a complex behavioral feature of a walking person,including the time the foot touch and leave the ground,the human height,and the swing amplitude of hands.Compared with face recognition,gait recognition has the following advantages.Firstly,the distance applicable to gait recognition is longer,while the recognition difficulty of facial features increases as the detection distance increases.Secondly,gait feature recognition is non-active,and workers on the operation scene are walking almost anytime and anywhere,but face recognition expects the recognition object to face the detection device.Thirdly,gait features have strong specificity and are difficult to be imitated and modified.Nevertheless,gait information cannot be used alone for identity verification of workers in static poses.To solve the above problems,a multi-feature fusion identity verification method was proposed,which combined multiple features such as gait and face features for recognition without being interfered by external factors such as clothing and environment.This method effectively improved the accuracy of identity verification.This multi-feature identity verification method,combining face recognition and gait recognition,included identity registration phase,training phase and test phase.In the registration phase,the face and gait information were manually marked and recorded in the database.In the training phase,the correlated network was used to extract the gait contour map and face region of the image sequence in the video.Then the deep learning network model was used to extract relevant features in order to build the relationship between the fused feature vector and the identity ID.In the test phase,whether there is a clear face in the image was judged.If so,the multi-feature fusion recognition method was used.Otherwise,only the gait feature for feature matching was used to complete the identity verification.The results show that the proposed multi-feature fusion method achieves the classification accuracy of 99.17% on the CASIA-A data set of the Institute of Automation,Chinese Academy of Sciences.The classification accuracy is 98.75%,100% and 98.75% in the three views included in the dataset.Therefore,the proposed method can effectively improve the accuracy of identification in single-person scenes,thus providing an effective scheme for identity verification in outdoor work scenes. |
