Name
Abstract | ||
|---|---|---|
In this paper we propose a novel approach to detect and track moving entities in wide surveillance video. Considering the wide area covered by the camera, which makes the detection and tracking of humans, as well as the classification of their motion a complex task and resource consuming, we adopt a particle-based approach to highlight particles of interest and group them based on their motion properties. A crossinfluence matrix is computed at the particle level identifying the relevant areas of the video, and pruning static particles and outliers. Based on the motion features of the particles marked as interacting with their neighbors, a learning procedure based on an MLP neural network is implemented, in order to create consistent groups, representing the moving entities to be tracked over time. The method has been tested on two publicly available datasets with different resolutions and motion characteristics. |
Year | Venue | Keywords |
|---|---|---|
2013 | 2013 PROCEEDINGS OF THE 21ST EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | Particle tracking, entity influence, social interactions |
Field | DocType | Citations |
Computer vision,Computer science,Matrix (mathematics),Outlier,Video tracking,Artificial intelligence,Artificial neural network,Particle | Conference | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Rota | 1 | 44 | 7.64 |
| Habib Ullah | 2 | 26 | 5.59 |
| Nicola Conci | 3 | 149 | 31.63 |
| Nicu Sebe | 4 | 7013 | 403.03 |
| De Natale Francesco | 5 | 262 | 40.77 |