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| M. Alterman, Y. Y. Schechner, P. Perona, J. Shamir, "Detecting Motion through Dynamic Refraction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 245-251, Jan., 2013. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.2012.192, author = {M. Alterman and Y. Y. Schechner and P. Perona and J. Shamir}, title = {Detecting Motion through Dynamic Refraction}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {35}, number = {1}, issn = {0162-8828}, year = {2013}, pages = {245-251}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.192}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Detecting Motion through Dynamic Refraction IS - 1 SN - 0162-8828 SP245 EP251 EPD - 245-251 A1 - M. Alterman, A1 - Y. Y. Schechner, A1 - P. Perona, A1 - J. Shamir, PY - 2013 KW - refraction KW - atmospheric turbulence KW - feature extraction KW - image motion analysis KW - object detection KW - object tracking KW - moving object point discrimination KW - motion detection KW - dynamic refraction KW - random dynamic distortions KW - atmospheric turbulence KW - water interface KW - attention-drawing periscope KW - field of view KW - FOV KW - object detection KW - object tracking KW - motion feature KW - Optical distortion KW - Cameras KW - Nonlinear distortion KW - Covariance matrix KW - Dynamics KW - Animals KW - Vectors KW - distortion KW - Motion detection KW - refraction KW - random media KW - classification VL - 35 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
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Refraction causes random dynamic distortions in atmospheric turbulence and in views across a water interface. The latter scenario is experienced by submerged animals seeking to detect prey or avoid predators, which may be airborne or on land. Man encounters this when surveying a scene by a submarine or divers while wishing to avoid the use of an attention-drawing periscope. The problem of inverting random refracted dynamic distortions is difficult, particularly when some of the objects in the field of view (FOV) are moving. On the other hand, in many cases, just those moving objects are of interest, as they reveal animal, human, or machine activity. Furthermore, detecting and tracking these objects does not necessitate handling the difficult task of complete recovery of the scene. We show that moving objects can be detected very simply, with low false-positive rates, even when the distortions are very strong and dominate the object motion. Moreover, the moving object can be detected even if it has zero mean motion. While the object and distortion motions are random and unknown, they are mutually independent. This is expressed by a simple motion feature which enables discrimination of moving object points versus the background.
Index Terms:
refraction,atmospheric turbulence,feature extraction,image motion analysis,object detection,object tracking,moving object point discrimination,motion detection,dynamic refraction,random dynamic distortions,atmospheric turbulence,water interface,attention-drawing periscope,field of view,FOV,object detection,object tracking,motion feature,Optical distortion,Cameras,Nonlinear distortion,Covariance matrix,Dynamics,Animals,Vectors,distortion,Motion detection,refraction,random media,classification
Citation:
M. Alterman, Y. Y. Schechner, P. Perona, J. Shamir, "Detecting Motion through Dynamic Refraction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 245-251, Jan. 2013, doi:10.1109/TPAMI.2012.192
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