Flying objects detection from a single moving camera

We 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.

[1]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Deva Ramanan,et al.  Exploring Weak Stabilization for Motion Feature Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Marc Pollefeys,et al.  Stereo depth map fusion for robot navigation , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Roland Siegwart,et al.  Monocular Vision for Long‐term Micro Aerial Vehicle State Estimation: A Compendium , 2013, J. Field Robotics.

[6]  Bernt Schiele,et al.  New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Bálint Vanek,et al.  Visual Detection and Implementation Aspects of a UAV See and Avoid System , 2011, 2011 20th European Conference on Circuit Theory and Design (ECCTD).

[8]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[11]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Roland Siegwart,et al.  A robust and modular multi-sensor fusion approach applied to MAV navigation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Pascal Fua,et al.  Fast Object Detection with Entropy-Driven Evaluation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Andrew Zisserman,et al.  Image Classification using Random Forests and Ferns , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Marc Pollefeys,et al.  PIXHAWK: A system for autonomous flight using onboard computer vision , 2011, 2011 IEEE International Conference on Robotics and Automation.

[16]  Pascal Fua,et al.  Making Action Recognition Robust to Occlusions and Viewpoint Changes , 2010, ECCV.

[17]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[18]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[19]  Hongyuan Zha,et al.  A General Boosting Method and its Application to Learning Ranking Functions for Web Search , 2007, NIPS.

[20]  Yair Weiss,et al.  Learning object detection from a small number of examples: the importance of good features , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[21]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[22]  Davide Scaramuzza,et al.  SVO: Fast semi-direct monocular visual odometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[23]  G. Conte,et al.  An Integrated UAV Navigation System Based on Aerial Image Matching , 2008, 2008 IEEE Aerospace Conference.

[24]  Moongu Jeon,et al.  A New Framework for Background Subtraction Using Multiple Cues , 2012, ACCV.

[25]  Miguel A. Olivares-Méndez,et al.  On-board and Ground Visual Pose Estimation Techniques for UAV Control , 2011, J. Intell. Robotic Syst..