Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy
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Vehicle behavior analysis is often based on the motion trajectory analysis, which lays the foundation for many applications such as velocity detection, vehicle classification, and vehicle counting. In this paper, a trajectory clustering framework is proposed for vehicle trajectory analysis. Firstly, feature points are extracted by ORB algorithm which uses binary strings as an efficient feature point descriptor. Secondly, a matching method based on Hamming distance is used to obtain the tracking trajectory points. Finally, a novel clustering method, which contains three phrases, i.e., coarse clustering, fine clustering, and agglomerative clustering, is proposed to classify vehicle trajectory points based on the 3D information in real traffic video. By applying this clustering method in actual traffic scenes, much more stable clustering results can be obtained compared with other methods. Experimental results demonstrate that the accuracy of the proposed method can reach 95%. Furthermore, vehicle type can be estimated to realize vehicle classification.
KeywordsTrajectory analysis 3D information ORB Trajectory clustering Vehicle classification
This work is supported by the National Natural Science Fund of China (No. 61572083), the Natural Science Foundation of Shaanxi Province (Nos. 2015JQ6230, 2015JZ018), and the Fundamental Research Funds for the Central Universities of China (Nos. 310824163411, 310824171003).
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Alcantarilla PF, Bartoli A, Davison AJ (2012) Kaze features. In: Proceedings of European conference on computer visionGoogle Scholar
- Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: Proceedings of European conference on computer visionGoogle Scholar
- Beymer D, McLauchlan P, Coifman B, Malik J (1997) A real-time computer vision system for measuring traffic parameters. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 495–501Google Scholar
- Calonder M, Lepetit V, Strecha C, Fua P (2010) Brief: binary robust independent elementary features. In: Proceedings of European conference on computer visionGoogle Scholar
- Forssén PE, Lowe DG (2007) Shape descriptors for maximally stable extremal regions. In: Proceedings of IEEE conference on computer visionGoogle Scholar
- Fu Z, Hu W, Tan T (2005) Similarity based vehicle trajectory clustering and anomaly detection. In: IEEE international conference on image processing, ICIP, pp 602–605Google Scholar
- Harris C, Stephens M (1988) combined corner and edge detector. In: Proceedings of the Alvey vision conference, pp 147–151Google Scholar
- Leutenegger S, Chli M, Siegwart R (2011) BRISK: binary robust invariant scalable keypoints. In: Proceedings of IEEE conference on computer visionGoogle Scholar
- Li X, Hu W, Hu W (2006) A coarse-to-fine strategy for vehicle motion trajectory clustering. In: 18th International conference on pattern recognition (ICPR’06), vol 1, pp 591–594Google Scholar
- Meng F, You F (2013) A tracking algorithm based on ORB. In: Proceedings of international conference on mechatronic sciences, electric engineering and computer, pp 1187–1190Google Scholar
- Rabaud V, Belongie S (2006) Counting crowded moving objects. In: Proceedings of IEEE conference on computer vision and pattern recognitionGoogle Scholar
- Rosten E, Drummond T (2006) Machine learning for high speed corner detection. In: Proceedings of European conference on computer vision, pp 2091–2096Google Scholar
- Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: Proceedings of IEEE conference on computer vision, pp 2564–2571Google Scholar