Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos

Abstract

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.

References in corpus (1)

• High-Speed Tracking with Kernelized Correlation Filters

Cited by in corpus (4)

• Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
• Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model
• Spatial-Temporal Map Vehicle Trajectory Detection Using Dynamic Mode Decomposition and Res-UNet+ Neural Networks
• Trajectory-based Scene Understanding using Dirichlet Process Mixture Model