leave a trace - A People Tracking System Meets Anomaly Detection

TL;DR

This paper presents a system for real-time, anonymous detection of atypical and potentially dangerous situations in video surveillance by tracking trajectories, developed using a large, ethically collected dataset over three years.

Contribution

It introduces a novel approach combining people tracking with anomaly detection in a real-time, privacy-preserving manner using a large, ethically gathered dataset.

Findings

Effective detection of atypical trajectories in real-time

Development of a privacy-preserving tracking and detection system

Large dataset spanning three years for robust analysis

Abstract

Video surveillance always had a negative connotation, among others because of the loss of privacy and because it may not automatically increase public safety. If it was able to detect atypical (i.e. dangerous) situations in real time, autonomously and anonymously, this could change. A prerequisite for this is a reliable automatic detection of possibly dangerous situations from video data. This is done classically by object extraction and tracking. From the derived trajectories, we then want to determine dangerous situations by detecting atypical trajectories. However, due to ethical considerations it is better to develop such a system on data without people being threatened or even harmed, plus with having them know that there is such a tracking system installed. Another important point is that these situations do not occur very often in real, public CCTV areas and may be captured properly even less. In the artistic project leave a trace the tracked objects, people in an atrium of a institutional building, become actor and thus part of the installation. Visualisation in real-time allows interaction by these actors, which in turn creates many atypical interaction situations on which we can develop our situation detection. The data set has evolved over three years and hence, is huge. In this article we describe the tracking system and several approaches for the detection of atypical trajectories.