  Appropriate modeling of a surveillance scene is essential for detection of
anomalies in road traffic. Learning usual paths can provide valuable insight
into road traffic conditions and thus can help in identifying unusual routes
taken by commuters/vehicles. If usual traffic paths are learned in a
nonparametric way, manual interventions in road marking road can be avoided. In
this paper, we propose an unsupervised and nonparametric method to learn
frequently used paths from the tracks of moving objects in $\Theta(kn)$ time,
where $k$ denotes the number of paths and $n$ represents the number of tracks.
In the proposed method, temporal dependencies of the moving objects are
considered to make the clustering meaningful using Temporally Incremental
Gravity Model (TIGM). In addition, the distance-based scene learning makes it
intuitive to estimate the model parameters. Further, we have extended TIGM
hierarchically as Dynamically Evolving Model (DEM) to represent notable traffic
dynamics of a scene. 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 various state-of-the-art methods. We
have also highlighted the advantages of the proposed method over existing
techniques popularly used for designing traffic monitoring applications. It can
be used for administrative decision making to control traffic at junctions or
crowded places and generate alarm signals, if necessary.
