Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (15): 251-258.DOI: 10.3778/j.issn.1002-8331.1905-0194

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Highway Vehicle Detection Based on Sparse Trajectory Clustering

YANG Lu, SONG Huansheng, ZHANG Zhaoyang   

  1. School of Information Engineering, Chang’an University, Xi’an 710064, China
  • Online:2020-08-01 Published:2020-07-30



  1. 长安大学 信息工程学院,西安 710064


To solve the real-time vehicle detection problem in expressway, a vehicle detection method based on trajectory sparse spectral clustering is proposed. The main processing adopts ORB algorithm to detect feature points, tracks the trajectory of these feature points by Pyramid LK optical flow algorithm. The next step is inversely projecting trajectory to 3D world coordinate system. At the same time, similarity matrix is constructed with 3D trajectories information and is processed sparsely. After preliminary clustering the feature points, vehicle detection result is obtained by merging classes. Experimental results show that proposed method effectively solves vehicle occlusion problem and vehicle detection accuracy is improved to 93%, which has certain validity and value.

Key words: vehicle detection, trajectory clustering, spectral clustering, sparsity



关键词: 车辆检测, 轨迹聚类, 谱聚类, 稀疏化