Abstract
Appropriate modeling of a surveillance scene is essential for detection of anomalies in road traffic. Learning usual paths can provide valuable insight of road traffic conditions and thus can help in identifying abnormal routes taken by commuters/vehicles. If usual traffic paths are learned in a nonparametric way, manual interventions for marking roads can be avoided. We propose an unsupervised and nonparametric method to learn frequently used paths from the tracks of moving objects in $\Theta(kn)$ time, where $k$ is the number of paths and $n$ represents the number of tracks. In the proposed method, temporal dependencies of the moving objects are taken into consideration to make the clustering meaningful using Temporally Incremental Gravity Model (TIGM)-Dynamic State Model (DSM). In addition, the distance-based scene learning makes it realistically intuitive to estimate the model parameters. Experimental validation reveals that the proposed method can learn a scene quickly without prior knowledge about the number of paths ($k$). We have compared the results with state-of-the-art methods. We also highlight the advantages of the proposed method over these techniques, especially for traffic monitoring applications. Further, we extend the model to represent notable traffic dynamics of a scene, that can be used for administrative decision making to control traffic at junctions or crowded places. We have also applied the proposed model to understand its effectiveness in clustering network traffic data.
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URL
http://arxiv.org/abs/1803.06613