Abstract
Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers’ behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process (DDCRP), a trajectory analysis system that simultaneously performs trajectory clustering and path modelling was proposed. Unlike most traditional approaches where the number of clusters should be known, the proposed method decides the number of clusters automatically. The proposed algorithm was tested on two publicly available trajectory datasets, and the experimental results recorded better performance and considerable improvement in both datasets for the task of trajectory clustering compared to traditional approaches. The study proved that the proposed method is an appropriate candidate to be used for trajectory clustering and path modelling.
Highlights
The trajectory of a moving object obtained by tracking the object’s position from one frame to the is a simple yet efficient descriptor of an object’s motion
With bag-of-word representation alone long-term dependencies between observation cannot be captured which results in having partial path models in existing Probabilistic Topic Models (PTM) approaches. We addressed these problems by using similarity between trajectories as the prior probability in dependent Chinese restaurant process (DDCRP)
These samples were discarded in this study and we evaluated the proposed model on 9,500 trajectories in the test set with legal activities (Fig. 1)
Summary
The trajectory of a moving object obtained by tracking the object’s position from one frame to the is a simple yet efficient descriptor of an object’s motion. Trajectory analysis has long been a research focus in different fields of study (Jonsen, Myers & Flemming, 2003; Pao et al, 2012; Reed et al, 1999; Fox, Sudderth & Willsky, 2007). In the context of intelligent surveillance systems (ITS) (Tian et al, 2017), trajectory clustering is a critical core technology in many surveillance applications including activity analysis (Morris & Trivedi, 2011), path modelling (Zhang, Lu & Li, 2009), anomaly detection (Dee & Velastin, 2008), and road intersection traffic monitoring (Aköz & Karsligil, 2014). Many trajectory analysis systems consist of two main steps. Trajectories are grouped into clusters based on their similarities. After the trajectories are clustered, the path taken by agents
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