Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Survey of NOMA for VLC Systems: Research Challenges and Future Trends.

Sensors (Basel, Switzerland)·2022

Same author

Enhancing Optical Correlation Decision Performance for Face Recognition by Using a Nonparametric Kernel Smoothing Classification.

Sensors (Basel, Switzerland)·2019

Same author

Enhancing underwater optical imaging by using a low-pass polarization filter.

Optics express·2019

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 13, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment.

Amaury Auguste^1,2, Wissam Kaddah¹, Marwa Elbouz¹

¹L@bISEN, Equipe LSL, Yncrea Ouest, 20 Rue Cuirasse Bretagne, 29200 Brest, France.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This summary is machine-generated.

This study enhances urban behavioral analysis using video surveillance. Kalman filters provide more reliable people tracking than proximity methods, even with occlusions or crossings.

Keywords:

Kalman filters YOLO anonymity behavioral analysis clustering data analysis video tracking

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: Oct 13, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

Computer Vision
Artificial Intelligence
Urban Surveillance Systems

Background:

Improving behavioral analysis in urban environments is crucial for safety and management.
Existing tracking methods struggle with occlusions and individuals crossing paths in video data.

Purpose of the Study:

To propose and evaluate a novel people tracking method for urban video surveillance.
To compare the efficacy of a proximity-based tracking approach with a Kalman filter-based method.

Main Methods:

Developed two tracking approaches: proximity-based comparison of positions between frames.
Implemented a Kalman filter-based method for predicting individual positions in subsequent images.
Integrated distance concepts from the proximity method into the Kalman filter approach.

Main Results:

Kalman filter tracking proved more robust, successfully handling occlusions and person crossings.
The enhanced Kalman filter method improved both tracking accuracy and abnormal behavior detection.
Experimental results confirmed the Kalman filter method's superiority over the proximity method alone.

Conclusions:

Kalman filters offer a reliable approach for people tracking in complex urban surveillance scenarios.
The integration of distance metrics enhances Kalman filter performance for improved behavioral analysis.
The proposed methods provide valuable insights into speed and trajectory variations for urban monitoring.