Effect of contextual information in human action recognition in videos
With the growing threat of terrorism around the world, many law enforcement agencies have adopted computer vision technique to improve their operation efficiency. Human action recognition has been in the spotlight for the past few years due to its wide variety of capabilities such as gestures-based...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2016
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| Online Access: | http://hdl.handle.net/10356/67455 |
| _version_ | 1824455642449969152 |
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| author | Koh, Hong Wei |
| author2 | Teoh Eam Khwang |
| author_facet | Teoh Eam Khwang Koh, Hong Wei |
| author_sort | Koh, Hong Wei |
| collection | NTU |
| description | With the growing threat of terrorism around the world, many law enforcement agencies have adopted computer vision technique to improve their operation efficiency. Human action recognition has been in the spotlight for the past few years due to its wide variety of capabilities such as gestures-based and surveillance applications. The accuracy of recognizing human actions under different environments have improved tremendously by making use of contextual information to provide relevant clues which further examines human actions. Owing to the element of huge intra-class disparities, it is challenging to recognize the features of every individual.
This report focuses on the procedures of capturing and integrating contextual information with the feature covariance matrix to build a framework for analyzing its effect in human action recognition. The first phase of the project is to extract features from the videos using log covariance matrix with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) as the classifiers to discriminate the actions.
In the second phase of the project, the motion context descriptor was extracted from every single frame in the videos and integrated with the feature covariance. The combination of both features allows us to achieve an accuracy of over 95%. Additionally, a study into the ability to handle occlusion and body posture variation has been carried out using dense trajectory. |
| first_indexed | 2025-02-19T03:41:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/67455 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:41:27Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/674552023-07-07T15:58:33Z Effect of contextual information in human action recognition in videos Koh, Hong Wei Teoh Eam Khwang School of Electrical and Electronic Engineering DRNTU::Engineering With the growing threat of terrorism around the world, many law enforcement agencies have adopted computer vision technique to improve their operation efficiency. Human action recognition has been in the spotlight for the past few years due to its wide variety of capabilities such as gestures-based and surveillance applications. The accuracy of recognizing human actions under different environments have improved tremendously by making use of contextual information to provide relevant clues which further examines human actions. Owing to the element of huge intra-class disparities, it is challenging to recognize the features of every individual. This report focuses on the procedures of capturing and integrating contextual information with the feature covariance matrix to build a framework for analyzing its effect in human action recognition. The first phase of the project is to extract features from the videos using log covariance matrix with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) as the classifiers to discriminate the actions. In the second phase of the project, the motion context descriptor was extracted from every single frame in the videos and integrated with the feature covariance. The combination of both features allows us to achieve an accuracy of over 95%. Additionally, a study into the ability to handle occlusion and body posture variation has been carried out using dense trajectory. Bachelor of Engineering 2016-05-17T03:12:10Z 2016-05-17T03:12:10Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67455 en Nanyang Technological University 137 p. application/pdf |
| spellingShingle | DRNTU::Engineering Koh, Hong Wei Effect of contextual information in human action recognition in videos |
| title | Effect of contextual information in human action recognition in videos |
| title_full | Effect of contextual information in human action recognition in videos |
| title_fullStr | Effect of contextual information in human action recognition in videos |
| title_full_unstemmed | Effect of contextual information in human action recognition in videos |
| title_short | Effect of contextual information in human action recognition in videos |
| title_sort | effect of contextual information in human action recognition in videos |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/67455 |
| work_keys_str_mv | AT kohhongwei effectofcontextualinformationinhumanactionrecognitioninvideos |