Wednesday, February 04, 2015

Reconstruction-free action inference from compressive imagers

Kuldeep Kulkarni  just sent me the following:
Dear Igor...,

I am Kuldeep Kulkarni, a third year Phd student in Arizona State University. I would like to bring your kind attention to our recent submission to PAMI, titled 'Reconstruction-free action inference from compressive imagers'. It is uploaded on arxiv. We will greatly appreciate if you can post regarding the paper on your blog. Thanks and regards

Kuldeep Kulkarni
 Thanks Kuldeep. It is indeed very nice to see some progress in that realm !

Reconstruction-free action inference from compressive imagers by Kuldeep Kulkarni, Pavan Turaga

Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant challenges for inference in terms of storage, communication and computation. Compressive cameras have emerged as a potential solution to deal with the data deluge issues in such applications. However, inference tasks such as action recognition require high quality features which implies reconstructing the original video data. Much work in compressive sensing (CS) theory is geared towards solving the reconstruction problem, where state-of-the-art methods are computationally intensive and provide low-quality results at high compression rates. Thus, reconstruction-free methods for inference are much desired. In this paper, we propose reconstruction-free methods for action recognition from compressive cameras at high compression ratios of 100 and above. Recognizing actions directly from CS measurements requires features which are mostly nonlinear and thus not easily applicable. This leads us to search for such properties that are preserved in compressive measurements. To this end, we propose the use of spatio-temporal smashed filters, which are compressive domain versions of pixel-domain matched filters. We conduct experiments on publicly available databases and show that one can obtain recognition rates that are comparable to the oracle method in uncompressed setup, even for high compression ratios.  
I also like this upcoming title from Kuldeep "Real-time tracking from compressive cameras at 1% measurement rate"
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