@article {3294,
	title = {Coding of image feature descriptors for distributed rate-efficient visual correspondences.},
	journal = {International Journal of Computer Vision},
	volume = {94},
	year = {2011},
	month = {2011 Sep},
	pages = {267-81},
	abstract = {<p>Establishing visual correspondences is a critical step in many computer vision tasks involving multiple views of a scene. In a dynamic environment and when cameras are mobile, visual correspondences need to be updated on a recurring basis. At the same time, the use of wireless links between camera motes imposes tight rate constraints. This combination of issues motivates us to consider the problem of establishing visual correspondences in a distributed fashion between cameras operating under rate constraints. We propose a solution based on constructing distance preserving hashes using binarized random projections. By exploiting the fact that descriptors of regions in correspondence are highly correlated, we propose a novel use of distributed source coding via linear codes on the binary hashes to more efficiently exchange feature descriptors for establishing correspondences across multiple camera views. A systematic approach is used to evaluate rate vs visual correspondences retrieval performance; under a stringent matching criterion, our proposed methods demonstrate superior performance to a baseline scheme employing transform coding of descriptors.</p>
},
	issn = {0920-5691},
	doi = {10.1007/s11263-011-0427-1},
	author = {Ahammad, Parvez and Yeo, C and Ramchandran, K}
}
@proceedings {3304,
	title = {Compressed domain real-time action recognition.},
	year = {2006},
	month = {2006 Oct 3},
	abstract = {<p>We present a compressed domain scheme that is able to recognize and localize actions in real-time. The recognition problem is posed as performing a video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a large action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our classification results compare favorably with existing methods at only a fraction of their computational cost.</p>
},
	author = {Yeo, C and Ahammad, Parvez and Ramchandran, K and Sastry, S}
}
