A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera
Open Access
- 19 April 2012
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 4 (4), 1090-1111
- https://doi.org/10.3390/rs4041090
Abstract
We develop a real-time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera. To address the challenging characteristics of these vehicles, such as continuous unrestricted pose variation and low-frequency vibrations, new approaches must be developed. The main concept proposed in this work is to create an artificial optical flow field by estimating the camera motion between two subsequent video frames. The core of the methodology consists of comparing this artificial flow with the real optical flow directly calculated from the video feed. The motion of the UAV between frames is estimated with available parallel tracking and mapping techniques that identify good static features in the images and follow them between frames. By comparing the two optical flows, a list of dynamic pixels is obtained and then grouped into dynamic objects. Tracking these dynamic objects through time and space provides a filtering procedure to eliminate spurious events and misdetections. The algorithms have been tested with a quadrotor platform using a commercial camera.Keywords
This publication has 11 references indexed in Scilit:
- Comprehensive Utilization of Temporal and Spatial Domain Outlier Detection Methods for Mobile Terrestrial LiDAR DataRemote Sensing, 2011
- A Hybrid Moving Object Detection Method for Aerial ImagesLecture Notes in Computer Science, 2010
- TRAFFIC VIDEO-BASED MOVING VEHICLE DETECTION AND TRACKING IN THE COMPLEX ENVIRONMENTCybernetics and Systems, 2009
- Person Tracking in UAV VideoLecture Notes in Computer Science, 2008
- Human tracking and silhouette extraction for human–robot interaction systemsPattern Analysis and Applications, 2008
- Sensor fusion-based visual target tracking for autonomous vehicles with the out-of-sequence measurements solutionRobotics and Autonomous Systems, 2008
- Robust Background Subtraction with Foreground Validation for Urban Traffic VideoEURASIP Journal on Advances in Signal Processing, 2005
- Object tracking in image sequences using point featuresPattern Recognition, 2005
- Fast obstacle detection for urban traffic situationsIEEE Transactions on Intelligent Transportation Systems, 2002
- Algorithms for cooperative multisensor surveillanceProceedings of the IEEE, 2001