Trajectory Data Compression Algorithm Based on Motion State Changing | Zendy

Shuo Zhang | Zendy; Jian Zhang | Zendy; Lin Qi | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Trajectory Data Compression Algorithm Based on Motion State Changing

Author(s) -

Shuo Zhang,

Jian Zhang,

Lin Qi

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/6647074

Subject(s) - trajectory , computer science , compression (physics) , motion (physics) , data compression , object (grammar) , state (computer science) , key (lock) , computer vision , algorithm , similarity (geometry) , artificial intelligence , image (mathematics) , physics , materials science , computer security , astronomy , composite material

The trajectory information generated by the moving object plays an important role in studying the object movement. In this paper, a trajectory data compression algorithm based on the motion state changing is proposed to reduce trajectory data storage space and increase compression speed, which can accurately show the motion state and trajectory characteristics. This study has certain significance for the exploration of mass traffic data and the planning of traffic network. Combining the angle threshold with the velocity threshold of a moving object, the key data points are found and the redundant information is removed. Subsequently, the compressed trajectory is obtained. The experimental results show that the new algorithm can help to improve compression efficiency. The compressed trajectory has high similarity with the original trajectory in movement tendency.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research