# Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation

## TL;DR

This paper introduces a framework for crowd simulation and automatic data annotation for smart IoT cameras, enhancing pedestrian detection and behavior analysis through synthetic data and multi-camera support.

## Contribution

The paper presents a novel framework combining synthetic human agents, augmentation, and compositing for crowd simulation and data annotation in IoT-based video analysis.

## Key findings

- Validated with popular crowd datasets
- Supports multiple cameras and targets
- Improves data generation for training deep models

## Abstract

Smart video sensors for applications related to surveillance and security are IOT-based as they use Internet for various purposes. Such applications include crowd behaviour monitoring and advanced decision support systems operating and transmitting information over internet. The analysis of crowd and pedestrian behaviour is an important task for smart IoT cameras and in particular video processing. In order to provide related behavioural models, simulation and tracking approaches have been considered in the literature. In both cases ground truth is essential to train deep models and provide a meaningful quantitative evaluation. We propose a framework for crowd simulation and automatic data generation and annotation that supports multiple cameras and multiple targets. The proposed approach is based on synthetically generated human agents, augmented frames and compositing techniques combined with path finding and planning methods. A number of popular crowd and pedestrian data sets were used to validate the model, and scenarios related to annotation and simulation were considered.