# Monitoring of people entering and exiting private areas using Computer Vision

## TL;DR

This paper introduces a new dataset and a spatial transition-based method for monitoring people entering and exiting private areas using computer vision, addressing privacy concerns in surveillance.

## Contribution

It presents EnEx2, a pseudo-annotated dataset for entry-exit monitoring, and proposes a spatial transition-based event detection method for surveillance scenarios.

## Key findings

- The EnEx2 dataset effectively captures entry-exit scenarios.
- The proposed method achieves standard results on multiple datasets.
- The dataset encourages further research in privacy-preserving surveillance.

## Abstract

Entry-Exit surveillance is a novel research problem that addresses security concerns when people attain absolute privacy in camera forbidden areas such as toilets and changing rooms that are basic amenities to the humans in public places such as Shopping malls, Airports, Bus and Rail stations. The objective is, if not inside these camera forbidden areas, from outside, the individuals are to be monitored to analyze the time spent by them inside and also the suspecting transformations in their appearances if any. In this paper, firstly, a pseudo-annotated dataset of a laboratory observation of people entering and exiting the camera forbidden area captured using two cameras in contrast to the state-of-the-art single-camera based EnEx dataset is presented. Conventionally the proposed dataset is named \textbf{\textit{EnEx2}}. Next, a spatial transition based event detection to determine the entry or exit of individuals is presented with standard results by evaluating the proposed model using the proposed dataset and the publicly available standard video surveillance datasets that are hypothesized to Entry-Exit surveillance scenarios. The proposed dataset is expected to enkindle active research in Entry-Exit Surveillance domain.