| Video recognition is one of the common task scenarios in the field of computer vision.Unlike picture recognition,video data has both time domain and spatial domain information.How to make good use of the spatiotemporal features of video data while controlling the computational overhead is a difficult point for video recognition tasks and a breakthrough point for achieving performance improvement.Against violent terror video data,in addition to correctly identifying the violent scene,it is of great practical significance to accurately identify the personnel identity information in the video.However,uncontrollable video capture quality poses difficulties for personnel identity recognition.Usually the identification of people is achieved through face recognition.The angle of the face,the disturbance,etc.in the violent terror video will bring about a decrease in the recognition accuracy,and special face optimization processing is required to improve recognition accuracy.In order to solve the above problems,based on deep learning technology,this paper has carried out research and exploration on video scene recognition and face recognition optimization in the field of violent terror video recognition,including the following works:(1)Against the need to quickly identify video scenes for a large number of real-time videos,a scene recognition model for violent terror video was proposed to timely discover different categories of terror scenes.Recognition scenes are divided to bomb,shooting,fighting,crowd-running and normal scene.The model uses the average interval frame difference as the frame selection logic,and the conv-LSTM is the spatiotemporal feature extraction unit,which achieves a higher accuracy of violent terror scene recognition with less computational overhead.(2)Against the problem of poor face quality in the video of violent terror,a face correction method based on multi-face fusion is proposed to improve face recognition accuracy.The poor quality of face image acquisition in violent terror video is the main reason for the decline of recognition accuracy.Multi-face fusion is an effective method to solve the problem of single acquisition.The paper proposes a fusion correction method for multi-frame face images,and verifies its obvious advantages relative to multi-frame facial feature fusion.(3)Against the cobwebbing disturbance problem of suspect's credential photos,a credential photo cobwebbing disturbance elimination model was established to improve the comparison accuracy with faces in violent terror videos.The face photo on the credentials is often used as the criterion picture in face recognition system,but some of the identification photos are difficult to identify directly because of the cobwebbing disturbance for anti-counterfeiting.The paper proposes a disturbance elimination model based on the generative adversarial network,and guarantees the balance of the disturbance elimination and face identity retention by feature supervision,and the performance of comparison between label face and the face in the violent terror video has a higher guarantee.This paper focuses on two aspects of video scene recognition and face recognition optimization in the violent terror video.In video scene recognition,a recognition model with computational overhead guarantee and spatiotemporal feature combining is designed based on frame selection logic and conv-LSTM unit.In the face recognition optimization,the recognition performance of the face recognition model on the same dataset is improved with the participation of optimization model,and therefore the effectiveness of the method proposed in this paper on the optimization of the faces in the violent terror video and the elimination of cobwebbing disturbance on credential photos is illustrated. |
