  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 (TUIC)
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 similar cluster in subsequent frames. The algorithm is fast
and produces accurate results in $\Theta(kn)$ time, where $k$ is the number of
clusters and $n$ the number of pixels. Our experimental validation with
publicly available datasets reveals that the proposed framework has good
potential to open-up new opportunities for real-time traffic analysis.
