Leader identification is a crucial task in social analysis, crowd management and emergency planning. In this paper, we investigate a computational model for the individuation of leaders in crowded scenes. We deal with the lack of a formal definition of leadership by learning, in a supervised fashion, a metric space based exclusively on people spatiotemporal information. Based on Tarde’s work on crowd psychology, individuals are modeled as nodes of a directed graph and leaders inherits their relevance thanks to other members references. We note this is analogous to the way websites are ranked by the PageRank algorithm. A compact diagram depicting the input and the training process is shown in the figure below.
The code can be run as it from the main.m file. Features will be computed and the training will start.
The main code does not visualize results. In order to do that, you will need to download additional video/images data - please contact me if you need them.
If you find this code useful and use it in your research, please don't forget to cite it:
Solera, F.; Calderara, S.; Cucchiara, R., "Learning to Identify Leaders in Crowd"
Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Jun 2015
- Francesco Solera francesco.solera@unimore.it
- Simone Calderara simone.calderara@unimore.it
- Rita Cucchiara rita.cucchiara@unimore.it