| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
[2] Univ Massachusetts, Dept Comp Sci, Lowell, MA 01854 - USA
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS; v. 17, n. 3 JUL 2017. |
| Web of Science Citations: | 1 |
| Abstract | |
Recent technological advances in the acquisition and dissemination of videos have driven the development of several applications in the context of human action recognition, such as automatic surveillance, strategic planning, crime prevention and traffic monitoring, among others. Despite this large number of applications, several techniques found in the literature are specialized for a particular purpose, working only for a limited scope of actions. To improve the current scenario, this work proposes and evaluates the development of a flexible descriptor and a methodology for identifying human actions in different domains. The classification process utilizes a judgement mechanism for iteratively refining its outcome in order to converge to a decision that best fits the recognizer. Experiments are conducted on five public datasets with different characteristics, from events containing few actions to more complex scenarios involving a large number of people and interaction with objects. Results have demonstrated that the proposed approach provides a proper balance between computational speed and accuracy rate. Therefore, the developed prototype represents a promising tool for real-time applications. (AU) | |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/12228-1 - Detection and recognition of complex events in videos |
| Grantee: | Hélio Pedrini |
| Support Opportunities: | Scholarships abroad - Research |