Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
Hybrid Monte Carlo Filtering: Edge-Based People Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Object Localization by Bayesian Correlation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Coupled Hidden Semi Markov Models for Activity Recognition
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Evaluation of Background Subtraction Methods
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Motion Vector Estimation of Video Image by Pyramidal Implementation of Lucas Kanade Optical Flow
ICETET '09 Proceedings of the 2009 Second International Conference on Emerging Trends in Engineering & Technology
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
IEEE Transactions on Image Processing
Gaussian propagation model based dense optical flow for objects tracking
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Hi-index | 12.05 |
Considered as a weak point in road and railway infrastructure, level crossings (LC) improvement safety became an important field of academic research and took increasingly railways undertakings concerns. Improving safety of people and road-rail facilities is an essential key element to ensure a good operating of the road and railway transport. For this purpose, road and railway safety professionals from several countries have been focused on providing level crossings as safer as possible. Many actions are planned in order to exchange information and provide experiments for improving the management of level crossing safety and performance. This paper aims to develop a video surveillance system to detect, recognize and evaluate potentially dangerous situations in level crossing environments. First, a set of moving objects are detected and separated using an automatic clustering process coupled to an energy vector comparison strategy. Then, a multi-object tracking algorithm, based on optical flow propagation and Kalman filtering correction with adaptive parameters, is implemented. The next step consists on using a Hidden Markov Model to predict trajectories of the detected objects. Finally, the trajectories are analysed with a particular credibility model to evaluate dangerous situations at level crossings. Real data sets are used to test the effectiveness and robustness of the method. This work is developed within the framework of PANsafer project, supported by the French work program ANR.