action change detection in video based on hog

Background and objectives: action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. a number of solutions proposed to solve action recognition problem. many of these frameworks suppose that each video sequence includes only one action class. therefore, we need to break down a video sequence into sub-sequences, each containing only a single action class. methods: in this paper, we develop an unsupervised action change detection method to detect the time of actions change, without classifying the actions. in this method, a silhouette-based framework will be used for action representation. this representation uses xt patterns. the xt pattern is a selected frame of xty volume. this volume is achieved by rotating the traditional space-time volume and displacing its axes. in xty volume, each frame consists of two axes (x) and time (t), and y value specifies the frame number. results: to test the performance of the proposed method, we created 105 artificial videos using the weizmann dataset, as well as time-continuous camera-captured video. the experiments have been conducted on this dataset. the precision of the proposed method was 98.13% and the recall was 100%. conclusion: the proposed unsupervised approach can detect action changes with a high precision. therefore, it can be useful in combination with an action recognition method for designing an integrated action recognition system.