Detecting Missed and Anomalous Action Segments Using Approximate String Matching Algorithm
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We forget action steps and perform some unwanted action movements as amateur performers during our daily exercise routine, dance performances, etc. To improve our proficiency, it is important that we get a feedback on our performances in terms of where we went wrong. In this paper, we propose a framework for analyzing and issuing reports of action segments that were missed or anomalously performed. This involves comparing the performed sequence with the standard action sequence and notifying when misalignments occur. We propose an exemplar based Approximate String Matching (ASM) technique for detecting such anomalous and missing segments in action sequences. We compare the results with those obtained from the conventional Dynamic Time Warping (DTW) algorithm for sequence alignment. It is seen that the alignment of the action sequences under conventional DTW fails in the presence of missed action segments and anomalous segments due to its boundary condition constraints. The performance of the two techniques has been tested on a complex aperiodic human action dataset with Warm up exercise sequences that we developed from correct and incorrect executions by multiple people. The proposed ASM technique shows promising alignment and missed/anomalous notification results over this dataset.
KeywordsMissed action Anomalous action Dynamic Time Warping Approximate String Matching
We thank Divya Srivastava, Arnav Chopra, Aayush Sarda, Kushagra Surana and Rohil Surana for contributing towards creating dataset of Warm Up Exercise videos and their constant support throughout the work.
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