|تعداد مشاهده مقاله||2,476,995|
|تعداد دریافت فایل اصل مقاله||1,745,891|
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|دوره 8، شماره 1، فروردین 2020، صفحه 135-144 اصل مقاله (969.39 K)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2020.6949.351|
|M. Fakhredanesh* ؛ S. Roostaie|
|Faculty of Electrical and Computer, Malek Ashtar University of Technology, Tehran, Iran|
|تاریخ دریافت: 16 اسفند 1397، تاریخ بازنگری: 26 تیر 1398، تاریخ پذیرش: 19 آذر 1398|
|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|
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