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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Dual-View Single-Shot Multibox Detector at Urban Intersections: Settings and Performance Evaluation.

Marta Lenatti¹, Sara Narteni^1,2, Alessia Paglialonga¹

¹CNR-IEIIT, 10129 Turin, Italy.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

This study introduces a multi-camera video content analysis system using a single-shot multibox detector (SSD) to enhance smart mobility safety. Employing two cameras improves detection accuracy and reliability for public transportation alerts.

Keywords:

object detection single-shot multibox detector smart mobility video content analysis

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Area of Science:

Computer Vision
Artificial Intelligence
Smart Mobility

Background:

Advancements in artificial intelligence (AI) enable sophisticated smart mobility solutions.
Current systems require enhanced detection and alert mechanisms for public transportation safety.

Purpose of the Study:

To develop and evaluate a multi-camera video content analysis (VCA) system for detecting vehicles, riders, and pedestrians.
To improve the accuracy and reliability of alerts for public transportation drivers.

Main Methods:

Utilized a single-shot multibox detector (SSD) network for object detection.
Implemented a multi-view fusion method with two cameras offering different fields of view (FOV).
Combined visual and quantitative approaches for system evaluation.

Main Results:

The two-camera system achieved a precision of 68% and recall of 84%, outperforming a single-camera system (62% precision, 86% recall).
Analysis indicated that missed and false alerts are typically transitory.
Spatial and temporal redundancy were shown to increase system reliability.

Conclusions:

A multi-camera VCA system with simple multi-view fusion enhances smart mobility safety.
The integration of spatial and temporal redundancy significantly boosts the overall reliability of the VCA system.