A novel motion detection approach for large FOV cameras
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
On-chip ego-motion estimation based on optical flow
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
Monocular online learning for road region labeling and object detection from a moving platform
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Error sources and their impact on c-velocity methods
Pattern Recognition and Image Analysis
Obstacles extraction using a moving camera
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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This paper proposes a method for estimating the egomotion of the vehicle and for detecting moving objects on roads by using a vehicle mounted monocular camera. There are two problems in ego-motion estimation. Firstly, a typical road scene contains moving objects such as other vehicles. Secondly, roads display fewer feature points compared to the number associated with background structures. In our approach, ego-motion is estimated from the correspondences of feature points extracted from various regions other than those in which objects are moving. After estimating the ego-motion, the three dimensional structure of the scene is reconstructed and any moving objects are detected. In our experiments, it has been shown that the proposed method is able to detect moving objects such as vehicles and pedestrians.