Omni-Directional Vision for Robot Navigation
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Ego-Motion and Omnidirectional Cameras
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Geometric Properties of Central Catadioptric Line Images and Their Application in Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of Omnidirectional and PTZ Cameras for Accurate Cooperative Tracking
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Tracking of Moving Objects by Using a Low Resolution Image
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
NBiS '08 Proceedings of the 2nd international conference on Network-Based Information Systems
Journal of Intelligent and Robotic Systems
Heterogeneous Fusion of Omnidirectional and PTZ Cameras for Multiple Object Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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Unmanned aerial vehicles (UAVs) are seeing widespread use in military, scientific, and civilian sectors in recent years. As the mission demands increase, these systems are becoming more complicated. Omnidirectional camera is a vision sensor that can captures 360° view in a single frame. In recent years omnidirectional camera usage has experienced a remarkable increase in many fields, where many innovative research has been done. Although, it is very promising, employment of omnidirectional cameras in UAVs is quite new. In this paper, an innovative sensory system is proposed, that has an omnidirectional imaging device and a pan tilt zoom (PTZ) camera. Such a system combines the advantages of both of the camera systems. The system can track any moving object within its 360° field of view and provide detailed images of it. The detection of the moving object has been accomplished by an adaptive background subtraction method implemented on the lowered resolution images of the catadioptric camera. A novel algorithm has also been developed to estimate the relative distance of the object with respect to the UAV, using tracking information of both of the cameras. The algorithms are implemented on an experimental system to validate the approach.