# Unsupervised Anomalous Trajectory Detection for Crowded Scenes

## TL;DR

This paper introduces an unsupervised clustering-based algorithm for detecting anomalous trajectories in crowded scenes, utilizing trajectory extraction, feature analysis, mean-shift clustering, and entropy-based anomaly detection.

## Contribution

It proposes a novel unsupervised method combining multiple features and clustering for effective anomaly detection in crowded scene videos.

## Key findings

- Accurate detection of anomalous trajectories in diverse crowd scenes
- Effective use of mean-shift clustering and Shannon entropy for anomaly identification
- Robust performance across different crowd motion patterns

## Abstract

We present an improved clustering based, unsupervised anomalous trajectory detection algorithm for crowded scenes. The proposed work is based on four major steps, namely, extraction of trajectories from crowded scene video, extraction of several features from these trajectories, independent mean-shift clustering and anomaly detection. First, the trajectories of all moving objects in a crowd are extracted using a multi feature video object tracker. These trajectories are then transformed into a set of feature spaces. Mean shift clustering is applied on these feature matrices to obtain distinct clusters, while a Shannon Entropy based anomaly detector identifies corresponding anomalies. In the final step, a voting mechanism identifies the trajectories that exhibit anomalous characteristics. The algorithm is tested on crowd scene videos from datasets. The videos represent various possible crowd scenes with different motion patterns and the method performs well to detect the expected anomalous trajectories from the scene.