| Autor: |
Vibha, L., Venkatesha, M., Prasanth, G. Rao, Suhas, N., Shenoy, P. Deepa, Venugopal, K. R., Patnaik, L. M. |
| Předmět: |
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| Zdroj: |
International MultiConference of Engineers & Computer Scientists 2008; 2008, p572-577, 6p |
| Abstrakt: |
Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance and human-machine interface. In this paper we present a framework for detecting some important but unknown knowledge like vehicle identification and traffic flow count. The objective is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow which assists in regulating traffic. The present algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes are recorded by a stationary camera. The method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Background subtraction is used which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. The resulting system robustly identifies vehicles at intersection, rejecting background and tracks vehicles over a specific period of time. Real-life traffic video sequences are used to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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