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| Robert Pless, Tomás Brodský, Yiannis Aloimonos, "Detecting Independent Motion: The Statistics of Temporal Continuity," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 768-773, August, 2000. | |||
| BibTex | x | ||
| @article{ 10.1109/34.868679, author = {Robert Pless and Tomás Brodský and Yiannis Aloimonos}, title = {Detecting Independent Motion: The Statistics of Temporal Continuity}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {22}, number = {8}, issn = {0162-8828}, year = {2000}, pages = {768-773}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.868679}, 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 Independent Motion: The Statistics of Temporal Continuity IS - 8 SN - 0162-8828 SP768 EP773 EPD - 768-773 A1 - Robert Pless, A1 - Tomás Brodský, A1 - Yiannis Aloimonos, PY - 2000 KW - Independent motion KW - normal flow KW - tracking KW - video mosaic KW - stable feature frame KW - airborne visual surveillance. VL - 22 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—We consider a problem central in aerial visual surveillance applications—detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames, and the definition of a constraint violation function which acts as an indicator of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filter. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence.
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[15] http://www.cfar.umd.edu/users/brodsky/MPEG input.mpg.
[16] http://www.cfar.umd.edu/users/brodsky/MPEG objects.mpg.
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