In this project we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer’s attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarisation. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.

Input video streams:

Output of our system:

@article{napoletano2015attentive-monitoring,
 author = {Napoletano, Paolo and Boccignone, Giuseppe and Tisato, Francesco},
 year = {2015},
 month = {1},
 pages = {3266-3281},
 title = {Attentive Monitoring of Multiple Video Streams Driven by a Bayesian Foraging Strategy},
 volume = {24},
 number = {11},
 journal = {IEEE Transactions on Image Processing},
 pdf = {/download/napoletano2015attentive-monitoring.pdf},
 doi = {http://dx.doi.org/10.1109/TIP.2015.2431438},
 issn = {1057-7149},
 projectref = {http://www.ivl.disco.unimib.it/activities/multicamera-monitoring/}}

@inproceedings{napoletano2014attentive-multicamera,
 author = {Napoletano, Paolo and Tisato, Francesco},
 year = {2014},
 pages = {90240-},
 title = {An attentive multi-camera system},
 volume = {9024},
 publisher = {SPIE},
 booktitle = {Image Processing: Machine Vision Applications VII},
 doi = {10.1117/12.2042652}}