text: Moments capture a huge part of our lives. Accurate recognition of these moments is challenging due to the diverse and complex interpretation of the moments. Action recognition refers to the act of classifying the desired action activity present in a given video. In this work, we perform experiments on Moments in Time dataset to recognize accurately activities occurring in 3 second clips. We use state of the art techniques for visual, auditory and spatio temporal localization and develop method to accurately classify the activity in the Moments in Time dataset. Our novel approach of using Visual Based Textual features and fusion techniques performs well providing an overall 89.23 Top - 5 accuracy on the 20 classes - a significant improvement over the Baseline TRN model.
  @cite_14: In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter.
summary: Traditional approaches in activity recognition deals were performed on untrimmed videos. Thus, being able to localize the action in the video stream is important. The candidate temporal or spatio-temporal regions are referred as action proposals. Action proposals don't provide information about the class of an action but provides localize regions which are most likely to contain an action as mentioned in this extensive review @cite_14 paper . Action proposals (or non-action shots) prevent going through an exhaustive search space and helps detect temporal or spatio-temporal locations of actions.