Details
| Original language | English |
|---|---|
| Title of host publication | Outdoor and Large-Scale Real-World Scene Analysis - 15th International Workshop on Theoretical Foundations of Computer Vision, Revised Selected Papers |
| Pages | 1-26 |
| Number of pages | 26 |
| Publication status | Published - 2012 |
| Event | 15th International Workshop on Theoretical Foundations of Computer Vision - Dagstuhl Castle, Germany Duration: 26 Jun 2011 → 1 Jul 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7474 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Tracking methods mostly work with the assumption that each pedestrian moves independently unaware of the objects or the other pedestrians around it. In the real world though, it is clear that when walking in a crowd, pedestrians try to avoid collisions, keep a close distance to a group of friends or avoid static obstacles in the scene. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, including social and grouping behavior as a physical model within the tracking system, and second, using a global optimization scheme which takes into account all trajectories and all frames to solve the data association problem . Results are presented on three challenging publicly available datasets, showing our method outperforms state-of-the-art tracking systems. We also make a thorough analysis of the effect of the parameters of the proposed tracker as well as its robustness against noise, outliers and missing data.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Outdoor and Large-Scale Real-World Scene Analysis - 15th International Workshop on Theoretical Foundations of Computer Vision, Revised Selected Papers. 2012. p. 1-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7474 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Exploiting pedestrian interaction via global optimization and social behaviors
AU - Leal-Taixé, Laura
AU - Pons-Moll, Gerard
AU - Rosenhahn, Bodo
PY - 2012
Y1 - 2012
N2 - Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Tracking methods mostly work with the assumption that each pedestrian moves independently unaware of the objects or the other pedestrians around it. In the real world though, it is clear that when walking in a crowd, pedestrians try to avoid collisions, keep a close distance to a group of friends or avoid static obstacles in the scene. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, including social and grouping behavior as a physical model within the tracking system, and second, using a global optimization scheme which takes into account all trajectories and all frames to solve the data association problem . Results are presented on three challenging publicly available datasets, showing our method outperforms state-of-the-art tracking systems. We also make a thorough analysis of the effect of the parameters of the proposed tracker as well as its robustness against noise, outliers and missing data.
AB - Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Tracking methods mostly work with the assumption that each pedestrian moves independently unaware of the objects or the other pedestrians around it. In the real world though, it is clear that when walking in a crowd, pedestrians try to avoid collisions, keep a close distance to a group of friends or avoid static obstacles in the scene. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, including social and grouping behavior as a physical model within the tracking system, and second, using a global optimization scheme which takes into account all trajectories and all frames to solve the data association problem . Results are presented on three challenging publicly available datasets, showing our method outperforms state-of-the-art tracking systems. We also make a thorough analysis of the effect of the parameters of the proposed tracker as well as its robustness against noise, outliers and missing data.
UR - http://www.scopus.com/inward/record.url?scp=84867872873&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34091-8_1
DO - 10.1007/978-3-642-34091-8_1
M3 - Conference contribution
AN - SCOPUS:84867872873
SN - 9783642340901
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 26
BT - Outdoor and Large-Scale Real-World Scene Analysis - 15th International Workshop on Theoretical Foundations of Computer Vision, Revised Selected Papers
T2 - 15th International Workshop on Theoretical Foundations of Computer Vision
Y2 - 26 June 2011 through 1 July 2011
ER -