Abstract
A reliable and effective trajectories similarity metric is one of key factors for vehicle trajectories clustering problem. A trajectory clustering algorithm based on the rigid constraints of vehicles in 3-D space is proposed in this paper, which conducts vehicle trajectories clustering effectively and precisely by using a new 3-D trajectories similarity metric. Based on two key procedures, camera calibration and a reconstruction of 2-D trajectories in 3-D space, a valuable principle that the heights of the trajectories have a linear relationship between them is found through using the kinematic properties of vehicle rigid body in moving. A more valuable information need to be pay attention is that the height of two trajectories that with displacement difference satisfies a plane surface character in 3-D space when conducts a height enumeration. The experimental results show that the trajectories are very stable and reliable for clustering and event detection when reconstructing their relative position in 3-D world coordinate system.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grants 61572083, Key Program of Natural Science of Shannxi under Grants 2015JZ018 and the Fundamental Research Founds for Central University under Grants 310824151034.
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Jiang, W., Zhaoyang, Z., Huansheng, S., Fenglan, P. (2016). A Vehicle Trajectory Analysis Approach Based on the Rigid Constraints of Object in 3-D Space. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_4
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DOI: https://doi.org/10.1007/978-981-10-3002-4_4
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