Locality-constrained Multi-Instance Learning for Abnormal Trajectory Detection
- DOI
- 10.2991/iwmecs-15.2015.138How to use a DOI?
- Keywords
- Abnormal trajectory detection, locality-constrained trajectory partition, Hierarchical Dirichlet Process-Hidden Markov model (HDP-HMM), multi-instance learning.
- Abstract
Abnormal event detection based on trajectory has been extensively investigated in recent years; however, problems remain when processing an incomplete trajectory that usually has abnormality in some parts of the whole trajectory and the rest are normal. In this paper, we propose a locality-constrained multi-instance learning framework for abnormal trajectory detection. We explore local adaptability for robust trajectory classification, and partition each trajectory into tracklets by control points of cubic B-spline curves. Then, the tracklets are modeled by Hierarchical Dirichlet Process-Hidden Markov Model (HDP-HMM). Finally, the whole trajectory is considered within the multi-instance learning framework as bags, when abnormal ones are positive bags consist of tracklets, normal trajectories are negative bags with tracklets. With experimental results on the CAVIAR dataset, it shows that the proposed method achieves better performance than several recent approaches.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ruoyao Li PY - 2015/10 DA - 2015/10 TI - Locality-constrained Multi-Instance Learning for Abnormal Trajectory Detection BT - Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences PB - Atlantis Press SP - 691 EP - 696 SN - 2352-538X UR - https://doi.org/10.2991/iwmecs-15.2015.138 DO - 10.2991/iwmecs-15.2015.138 ID - Li2015/10 ER -