@conference {3298,
	title = {High speed action recognition and localization in compressed domain videos.},
	booktitle = {IEEE Transactions on Circuits and Systems for Video Technology: Special issue on Video Surveillance},
	year = {2008},
	month = {2008 Aug},
	abstract = {<p>We present a compressed domain scheme that is able to recognize and localize actions at high speeds. The recognition problem is posed as performing an action 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 directions and 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 benchmark action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our proposed method achieved a classification accuracy of 90\%, comparing favorably with existing methods in action classification accuracy, and is able to localize a template video of 80 x 64 pixels with 23 frames in a test video of 368 x 184 pixels with 835 frames in just 11 seconds, easily outperforming other methods in localization speed. We also perform a systematic investigation of the effects of various encoding options on our proposed approach. In particular, we present results on the compression-classification trade-off, which would provide valuable insight into jointly designing a system that performs video encoding at the camera front-end and action classification at the processing backend.</p>
},
	author = {Yeo, C and Ahammad, Parvez and Ramchandran, K and Sastry, S}
}
