Intelligent video analysis for enhanced pedestrian detection by hybrid metaheuristic approach
| Autor: | K. R. Sri Preethaa, A. Sabari |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Pedestrian detection Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computational intelligence 02 engineering and technology Machine learning computer.software_genre Theoretical Computer Science Support vector machine 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Geometry and Topology Artificial intelligence business Metaheuristic computer Software |
| Zdroj: | Soft Computing. 24:12303-12311 |
| ISSN: | 1433-7479 1432-7643 |
| Popis: | Intelligent video analytics for pedestrian detection plays a vital role for enhanced and effective surveillance system. Since smart city projects are gaining momentum in most of the countries nowadays, enhanced pedestrian detection plays a vital role in the field of security and surveillance. Various classification models were in existence for detecting the pedestrians which suffers from variety of challenges like illumination, pedestrian outfits, gestures, occlusion, lighting, etc., that affects the accuracy of detection. A strong feature vector describing the pedestrian is developed to enhance the accuracy of detection. In this paper, a novel hybrid metaheuristic pedestrian detection (HMPD) approach is proposed to enhance the accuracy of the classifier. HMPD extracts the working principles of support vector machine and genetic algorithm. The proposed model is trained using a set of human and non-human images. The accuracy of the proposed model is tested with benchmarking video data available at VISOR repository. The result clearly shows that HMPD approach produces the maximum accuracy than any traditional approaches. HMPD approach can further be applied in other domains for enhanced security and surveillance. |
| Databáze: | OpenAIRE |
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