Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos

Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state of objects temporally across frames. In this paper, a Gibbs sampling-based heuristic model referred to as temporal unknown incremental clustering has been proposed to cluster pixels with motion. Pixel motion is first detected using optical flow and a Bayesian algorithm has been applied to associate pixels belonging to a similar cluster in subsequent frames. The algorithm is fast and produces accurate results in Θ(kn) time, where k is the number of clusters and n the number of pixels. Our experimental validation with publicly available data sets reveals that the proposed framework has good potential to open up new opportunities for real-time traffic analysis.