Name
Abstract | ||
|---|---|---|
In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in order to reduce the bias between the number of tracks and the real number of targets. The main hypothesis is that those trajectories belonging to the same human body are more similar than trajectories belonging to different individuals. Several data representations and different distance/similarity measures are proposed and compared, under a common hierarchical clustering framework, and both quantitative and qualitative results are presented |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TCSVT.2006.879118 | IEEE Trans. Circuits Syst. Video Techn. |
Keywords | Field | DocType |
clustering technique,real number,lustering method,different individual,main hypothesis,trajectory clustering,common hierarchical clustering framework,automatic counting,human body,different distance,data representation,tracking system,hierarchical clustering | Hierarchical clustering,Similitude,Fuzzy clustering,Computer vision,Pedestrian,External Data Representation,Pattern recognition,Computer science,Tracking system,Artificial intelligence,Cluster analysis,Real number | Journal |
Volume | Issue | ISSN |
16 | 8 | 1051-8215 |
Citations | PageRank | References |
50 | 2.49 | 32 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianluca Antonini | 1 | 192 | 13.67 |
| Jean-Philippe Thiran | 2 | 2320 | 257.56 |