TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Temporal Search Algorithm for Multiple-Pedestrian Tracking |
| 영문제목(English Title) |
Temporal Search Algorithm for Multiple-Pedestrian Tracking |
| 저자(Author) |
Hye-Yeon Yu
Young-Nam Kim
Moon-Hyun Kim
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| 원문수록처(Citation) |
VOL 10 NO. 05 PP. 2310 ~ 2325 (2016. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this paper, we provide a trajectory-generation algorithm that can identify pedestrians in real time. Typically, the contours for the extraction of pedestrians from the foreground of images are not clear due to factors including brightness and shade; furthermore, pedestrians move in different directions and interact with each other. These issues mean that the identification of pedestrians and the generation of trajectories are somewhat difficult. We propose a new method for trajectory generation regarding multiple pedestrians. The first stage of the method distinguishes between those pedestrian-blob situations that need to be merged and those that require splitting, followed by the use of trained decision trees to separate the pedestrians. The second stage generates the trajectories of each pedestrian by using the point-correspondence method; however, we introduce a new point-correspondence algorithm for which the A* search method has been modified. By using fuzzy membership functions, a heuristic evaluation of the correspondence between the blobs was also conducted. The proposed method was implemented and tested with the PETS 2009 dataset to show an effective multiple-pedestrian-tracking capability in a pedestrian-interaction environment.
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| 키워드(Keyword) |
tracking search algorithm
machine learning
point correspondence
decision tree This
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| 파일첨부 |
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