Name
Abstract | ||
|---|---|---|
This paper presents a data-oriented tracking framework which aims to recover the spatio-temporal trajectories for an unknown number of interacting objects appearing and disappearing at arbitrary times. Data association is performed at three-levels of a hierarchy: (i) first, trajectory segments and an associated quality measure are generated by a local analysis of the space-time distribution of observations; (ii) a conservatively constrained association step links nearby consistent segments into intermediate trajectory fragments; and (iii) a last association step taking into account all available data (observations, trajectory fragments) generates the final trajectory estimates. The association step relies on the Hungarian algorithm and it also considers detection responses below the detection threshold as evidence associated with high ambiguity. We demonstrate the feasibility of the proposed approach applied to the pedestrian tracking task on two challenging datasets. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5651739 | 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING |
Keywords | Field | DocType |
multiple object tracking, pedestrian tracking, hierarchical data association, spatio-temporal tracking | Hungarian algorithm,Object detection,Computer vision,Markov process,Pattern recognition,Computer science,Sensor fusion,Video tracking,Temporal database,Artificial intelligence,Estimation theory,Trajectory | Conference |
ISSN | Citations | PageRank |
1522-4880 | 3 | 0.42 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Csaba Beleznai | 1 | 367 | 18.96 |
| David Schreiber | 2 | 51 | 5.78 |