A Feature Pair Based Method for Online Moving Object Detection from High-Resolution Airborne Videos

Current researches on moving object detection from airborne videos mainly focus on low-resolution videos. Recently, to achieve a wide-area of fine-grained surveillance, most of unmanned aerial vehicles (UAV) prefer capturing high-resolution videos. Traditional methods usually require the processing of many neighboring frames to achieve a good performance for detecting moving objects in a frame. However, for high-resolution videos, due to limited computing resources, it is difficult to process many frames online, making traditional methods unsuitable for this task. In this paper, we propose a feature pair based method for moving object detection from high-resolution videos. It only needs two frames to detect moving objects and most of operations are performed in the feature pair domain with high efficiency, being well suited for high-resolution situation. Moreover, we design four strategies, i.e., feature pair extraction, matched pair refinement, moving pair determination, and clustering strategies, to enable an accurate detection of moving objects. Experimental results demonstrate an excellent performance of the proposed method for moving object detection from high-resolution airborne videos.

[1]  Marc Van Droogenbroeck,et al.  Deep background subtraction with scene-specific convolutional neural networks , 2016, 2016 International Conference on Systems, Signals and Image Processing (IWSSIP).

[2]  Joonki Paik,et al.  Moving object detection using unstable camera for video surveillance systems , 2015 .

[3]  Witold Czajewski,et al.  Moving Objects Detection and Tracking Framework for UAV-based Surveillance , 2010, 2010 Fourth Pacific-Rim Symposium on Image and Video Technology.

[4]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

[5]  Robert T. Collins,et al.  Moving Object Localization in Thermal Imagery by Forward-Backward Motion History Images , 2009 .

[6]  Andrea Fusiello,et al.  Segmentation and tracking of multiple video objects , 2007, Pattern Recognit..

[7]  Young-Jun Son,et al.  Vision-Based Target Detection and Localization via a Team of Cooperative UAV and UGVs , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[8]  Martin Jägersand,et al.  MODNet: Motion and Appearance based Moving Object Detection Network for Autonomous Driving , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[9]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Zhiming Luo,et al.  Interactive deep learning method for segmenting moving objects , 2017, Pattern Recognit. Lett..

[11]  Patrick Bonnin,et al.  A robust real-time image algorithm for moving target detection from unmanned aerial vehicles (UAV) , 2014, 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO).

[12]  Gérard G. Medioni,et al.  Efficient detection and tracking of moving objects in geo-coordinates , 2010, Machine Vision and Applications.

[13]  Robert T. Collins,et al.  An Open Source Tracking Testbed and Evaluation Web Site , 2005 .

[14]  Pouria Sadeghi-Tehran,et al.  A real-time approach for autonomous detection and tracking of moving objects from UAV , 2014, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).

[15]  Shuxiao Li,et al.  Moving object detection in aerial video based on spatiotemporal saliency , 2013 .

[16]  Shuxiao Li,et al.  A dynamic online background modeling framework for moving object detection from airborne videos , 2015, 2015 IEEE International Conference on Progress in Informatics and Computing (PIC).

[17]  Gerhard Rigoll,et al.  A Deep Convolutional Neural Network for Background Subtraction , 2017, ArXiv.

[18]  Martial Hebert,et al.  A Flow-Based Approach to Vehicle Detection and Background Mosaicking in Airborne Video , 2005, CVPR.

[19]  Long Ang Lim,et al.  Foreground segmentation using convolutional neural networks for multiscale feature encoding , 2018, Pattern Recognit. Lett..

[20]  Michael Teutsch,et al.  Detection, Segmentation, and Tracking of Moving Objects in UAV Videos , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[21]  Long Ang Lim,et al.  Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding , 2018, Pattern Recognit. Lett..

[22]  Jin Young Choi,et al.  Detection of moving objects with a moving camera using non-panoramic background model , 2012, Machine Vision and Applications.