Name
Abstract | ||
|---|---|---|
Mining large-scale trajectory data streams (of moving objects) has been of ever increasing research interest due to an abundance of modern tracking devices and its large number of critical applications. In this paper, we are interested in mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining task that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. In particular, we focus on trajectories of pedestrians coming from motion video analysis and we are interested in interactive analysis and exploration of group dynamics, including various definitions of group gathering and dispersion. Towards this end, we present a suite of (three) tensor-based methods for efficient discovery of evolving groups of pedestrians. Traditional approaches to solve the problem heavily rely on well-defined clustering algorithms to discover groups of pedestrians at each time point, and then post-process these groups to discover groups that satisfy specific group pattern semantics, including time constraints. In contrast, our proposed methods are based on efficiently discovering pairs of pedestrians that move together over time, under varying conditions. Pairs of pedestrians are subsequently used as a building block for effectively discovering groups of pedestrians. The suite of proposed methods provides the ability to adapt to many different scenarios and application requirements. Furthermore, a query-based search method is provided that allows for interactive exploration and analysis of group dynamics over time and space. Through experiments on real data, we demonstrate the effectiveness of our methods on discovering group patterns of pedestrian trajectories against sensible baselines, for a varying range of conditions. In addition, a visual testing is performed on real motion video to assert the group dynamics discovered by each method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MDM.2018.00024 | 2018 19th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
Trajectory mining,Group pattern mining,pedestrian behavior | Pedestrian,Data stream mining,Time point,Suite,Computer science,Vehicle dynamics,Artificial intelligence,Cluster analysis,Trajectory,Semantics,Machine learning,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-4134-7 | 1 | 0.41 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdullah Sawas | 1 | 2 | 0.77 |
| Abdullah Abuolaim | 2 | 6 | 3.18 |
| Mahmoud Afifi | 3 | 35 | 10.85 |
| Manos Papagelis | 4 | 357 | 24.03 |