Camera Calibration from Video of a Walking Human
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking people across disjoint camera views by an illumination-tolerant appearance representation
Machine Vision and Applications
Real-time line detection through an improved Hough transform voting scheme
Pattern Recognition
Occlusion analysis: Learning and utilising depth maps in object tracking
Image and Vision Computing
A novel pixon-representation for image segmentation based on Markov random field
Image and Vision Computing
Putting Objects in Perspective
International Journal of Computer Vision
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we present a novel method to overcome the common constraint of traditional camera calibration methods of surveillance systems where all objects move on a single coplanar ground plane. The proposed method estimates a scene model with non-coplanar planes by measuring the variation of pedestrian heights across the camera FOV in a statistical manner. More specifically, the proposed method automatically segments the scene image into plane regions, estimates a relative depth and estimates the altitude for each image pixel, thus building up a 3D structure with multiple non-coplanar planes. By being able to estimate the non-coplanar planes, the method can extend the applicability of 3D (single or multiple camera) tracking algorithms to a range of environments where objects (pedestrians and/or vehicles) can move on multiple non-coplanar planes (e.g. multiple levels, overpasses and stairs).