An improved hybridized deep structured model for accurate video event recognition

Video event recognition plays an important role in the various research fields particularly in surveillance detection system. In the existing system it is done by deep hierarchical context model which utilizes several contextual data which contains various context information at level of feature, semantic and priority for the video recognition process. However, this research method might perform low with increased volume of videos and might be failed to predict the events accurately with less interrelation contextual features. The standstill challenges are solved via improved hybridized deep structured model. The three primary features of contextual data are to discriminate resultant neighborhood. Here hybrid textual perceptual descriptor and concept-based attribute extraction is performed for accurate recognition of video events. These extracted interaction context features are grouped by using improved K means algorithm. In addition, improved deep structured model that combines convolutional neural networks and conditional random fields are developed for learning middle level representations and mingle the bottom feature level, mid-semantic and top-level meanings for the identification of incidents. This proposed research method is evaluated by using VIRAT data set whose simulation analysis is performed using Matlab simulation toolkit. The overall evaluation of the proposed research method proves that the suggested method can provide better output in terms of accurate recognition of events.