1 research outputs found
Temporal Unknown Incremental Clustering (TUIC) 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 (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 time, where is the number of
clusters and 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