Semantic Segmentation based Crowd Tracking & Anomaly Detection

Project Overview

A neuro-fuzzy classifier-based surveillance system for semantic segmentation driven crowd tracking and anomaly detection in crowded public spaces. The framework integrates pixel-level semantic understanding with fuzzy inference rules to track individual movements and detect anomalous behaviors such as running, fighting, or crowding.