Research On Key Technologies Of Context-Aware Intelligent Human-Computer Interaction

With the popularity of smart devices,human-computer interaction has penetrated into every aspect of human production and life.Context-aware intelligent human-computer interaction has increasingly become a research hotspot in the field of artificial intelligence,machine vision,and data mining.Due to the universality of digital cameras and its related technologies,human-computer interaction technology based on monocular vision has attracted more and more researchers’ attention.Some related references at home and abroad have been read and investigated extensively.Human-computer interaction in complex situations is studied in depth aiming at some drawbacks and limitations for context-aware intelligent human-computer interaction based on monocular vision.Some main contributions of this dissertation are as follows:A context-aware illumination equalization method based on spatiotemporal structural similarity is proposed.The illumination compensation structure map and the object light reflection characteristics are utilized to obtain the illumination compensation spatial distribution.The time variation of illumination is estimated by calculating the illumination variation of the non-dynamic object between the two frames.Spatiotemporal illumination compensation and logarithm histogram equalization are developed to fast illumination equalization for video.The experimental results show that the proposed method can simultaneously improve the visibility,contrast,naturalness,illumination consistency,and information stability.A human segmentation method based on background modeling is proposed.According to the motion characteristics of the human body and the features of the head structure,the frame images containing no human body pixels are constructed by using the structural similarity distribution map and the head detection,which are used for background modeling.The background modeling algorithm and multi-features fusion segmentation algorithm are combined to obtain human body from video.The experimental results show that the proposed method can segment human body from video under uncontrolled scene in real time,accurately and completely.A 3D skeleton estimation method based on difference updating is proposed.The above human segmentation method and the skeleton locating algorithm are fused to improve the efficiency of skeleton locating and reduce the false detection of the key points of the skeleton.According to the motion consistency between body and skeleton,the difference update algorithm is developed to restrain the jitter phenomenon of skeleton positioning.Using the normalized relative position feature of the skeleton,the depth dictionary model is built to obtain the 3D skeleton.The experimental results show that the proposed method is effective and feasible.A face orientation estimation method based on pattern fusion facial key point localization is proposed.The two-scale facial region detection algorithm is developed to locate the facial regions from image.Utilizing the relative relationship of the region of facial key points,two different localization algorithms are fused to improve the accuracy of facial key point localization.A face orientation recognition model is built by using facial key points,which describe sparse facial features.Compared with face orientation estimation methods based on facial dense features,the proposed method has the robust ability of facial features description,which can effectively improve the accuracy of face orientation estimation.An interaction subject user perception method based on phased behavior characteristics is proposed.Skeleton points of the parts of human body are used to phased estimate whether the user has the interaction intention.The spatial nearest neighbor algorithm is utilized to locate the interaction subject user from the users who have interactive behavior.The experimental results show that the proposed method is effective and feasible.An interaction subject user tracking method based on human torso displacement is proposed.The feature of human torso displacement is produced to track the interaction subject user.Skeleton regions-based color histogram matching algorithm and fast positive face recognition algorithm are developed to recover the user tracking chain that is interrupted due to skeleton loss.Compared with the tracking methods based on the whole region of human,the proposed method can effectively solve the problem of tracking region overlap,which may cause the identity of user to be confused.A human-computer interaction method based on adaptive virtual space screen is proposed.According the face orientation of the interaction subject user,the interested region on the interaction interface is located.A virtual space screen is produced to map the interested region.Through virtual touch between hand and virtual space screen,the interaction intention of the interaction subject user is responded.