Motion Recognition Algorithm in VR Video Based on Dual Feature Fusion and Adaptive Promotion

VR video recognition in complex environment, a motion recognition algorithm based on two-feature fusion and adaptive enhancement is proposed to solve the problems of inaccurate target position, target drift and recognition error caused by the vulnerability to light change, target rotation and occlusion. First, based on the spatio-temporal context (STC) mechanism, image sequence features are extracted through spatio-temporal context relationship and visual system characteristics to reduce the influence of light changes and occlusion on behaviors. Secondly, reliable feature point tracks are obtained through image feature point tracking and background track cutting, and a rich set of action descriptors (AD) are calculated from which local motion information, shape and static appearance information of the track are retained. After that, the principal component analysis operator is introduced to define the double feature fusion rules, and the STC feature and AD feature are combined to form a more accurate and complete feature representation. Finally, adaptive boosting algorithm (ABA) is used to train the classification through the new features obtained and complete the decision judgment of behavior and action. The experimental results show that the proposed algorithm has higher recognition accuracy and robustness compared with the current commonly used motion recognition methods, and can better adapt to complex background and motion changes.