MTA 2009 – Content and Task-Based View Selection from Multiple Video Streams

| January 13, 2009

Fahad Daniyal, Murtaza Taj, Andrea Cavallaro
Multimedia Tools and Applications, 2009


We present a content-aware multi-camera selection technique that uses object and frame-level features. First objects are detected using a color-based change detector. Next trajectory information for each object is generated using multi-frame graph matching. Finally, multiple features including size and location are used to generate an object score. At frame-level, we consider total activity, event score, number of objects and cumulative object score. These features are used to generate score information using a multivariate Gaussian distribution. The best view is selected using a Dynamic Bayesian Network (DBN), which utilizes camera network information. DBN employs previous view information to select the current view thus increasing resilience to frequent switching. The performance of the proposed approach is demonstrated on three multi-camera setups with semi-overlapping fields of view: a basketball game, an indoor airport surveillance scenario and a synthetic outdoor pedestrian dataset. We compare the proposed view selection approach with a maximum score based camera selection criterion and demonstrate a significant decrease in camera flickering. The performance of the proposed approach is also validated through subjective testing.


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Text Reference:

F. Daniyal, M. Taj and A. Cavallaro,
"Content and task-based view selection from multiple video streams",
in Data semantics from multi-media systems, Special issue: semantic multimedia, vol. 46, no. 2-3, pp. 235-258, 2009.

Bibtex Reference:

title={Content and task-based view selection from multiple video streams},
author={F. Daniyal and M Taj and A Cavallaro},
month = {January},
volume = {2--3},
number = {46},
pages = {235--258},


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