000206719 001__ 206719
000206719 005__ 20190812205827.0
000206719 020__ $$a978-1-4673-6964-0
000206719 0247_ $$2doi$$a10.1109/CVPR.2015.7299040
000206719 037__ $$aCONF
000206719 245__ $$aFlying Objects Detection from a Single Moving Camera
000206719 269__ $$a2015
000206719 260__ $$c2015
000206719 336__ $$aConference Papers
000206719 520__ $$aWe propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. Solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach to motion stabilization of local image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As the problem is relatively new, we collected two challenging datasets for UAVs and Aircrafts, which can be used as benchmarks for flying objects detection and vision-guided collision avoidance.
000206719 6531_ $$aobject detection
000206719 6531_ $$aspatio-temporal features
000206719 6531_ $$amotion compensation
000206719 700__ $$0246627$$g222094$$aRozantsev, Artem
000206719 700__ $$0240235$$g149007$$aLepetit, Vincent
000206719 700__ $$aFua, Pascal$$g112366$$0240252
000206719 7112_ $$dJune 7-12, 2015$$cBoston, Massachusetts, USA$$aConference on Computer Vision and Pattern Recognition (CVPR)
000206719 773__ $$tProceedings of the IEEE Conference on Computer Vision and Pattern Recognition$$q4128-4136
000206719 8564_ $$zPreprint$$yPreprint$$uhttps://infoscience.epfl.ch/record/206719/files/2000.pdf$$s8721027
000206719 909C0 $$xU10659$$pCVLAB$$0252087
000206719 909CO $$ooai:infoscience.tind.io:206719$$qGLOBAL_SET$$pconf$$pIC
000206719 917Z8 $$x222094
000206719 917Z8 $$x222094
000206719 917Z8 $$x222094
000206719 917Z8 $$x222094
000206719 917Z8 $$x222094
000206719 937__ $$aEPFL-CONF-206719
000206719 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000206719 980__ $$aCONF