M. Fakhredanesh - Faculty of Electrical and Computer, Malek Ashtar University of Technology, Tehran, Iran
S. Roostaie - Faculty of Electrical and Computer, Malek Ashtar University of Technology, Tehran, Iran

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.

Artificial Intelligence, Computer vision, Machine Learning, Video surveillance, Motion analysis