Object tracking with dual field-of-view switching in aerial videos

Visual object tracking plays an important role in intelligent aerial surveillance by unmanned aerial vehicles (UAV). In ordinary applications, aerial videos are captured by cameras with a fixed-focus lens or a zoom lens, for which the field-of-view (FOV) of the camera is fixed or smoothly changed. In this paper, a special application of the visual tracking in aerial videos captured by the dual FOV camera is introduced, which is different from ordinary applications since the camera quickly switches its FOV during the capturing. Firstly, the tracking process with the dual FOV camera is analyzed, and a conclusion is made that the critical part for the whole process depends on the accurate tracking of the target at the moment of FOV switching. Then, a cascade mean shift tracker is proposed to deal with the target tracking under FOV switching. The tracker utilizes kernels with multiple bandwidths to execute mean shift locating, which is able to deal with the abrupt motion of the target caused by FOV switching. The target is represented by the background weighted histogram to make it well distinguished from the background, and a modification is made to the weight value in the mean shift process to accelerate the convergence of the tracker. Experimental results show that our tracker presents a good performance on both accuracy and efficiency for the tracking. To the best of our knowledge, this paper is the first attempt to apply a visual object tracking method to the situation where the FOV of the camera switches in aerial videos.

[1]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[2]  Jia-Yush Yen,et al.  High Performance FOV Switching Mechanism Design for an Infrared Zoom Lens , 2011 .

[3]  Hui Cheng,et al.  Vehicle and Person Tracking in Aerial Videos , 2007, CLEAR.

[4]  R. Venkatesh Babu,et al.  Robust object tracking with background-weighted local kernels , 2008, Comput. Vis. Image Underst..

[5]  Shuxiao Li,et al.  Adaptive pyramid mean shift for global real-time visual tracking , 2010, Image Vis. Comput..

[6]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Pascual Campoy Cervera,et al.  COLIBRI: A vision-Guided UAV for Surveillance and Visual Inspection , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[8]  Gaurav S. Sukhatme,et al.  Visually guided landing of an unmanned aerial vehicle , 2003, IEEE Trans. Robotics Autom..

[9]  Shao-Fa Li,et al.  An adaptive mean shift tracking method using multiscale images , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[10]  Junseok Kwon,et al.  Wang-Landau Monte Carlo-Based Tracking Methods for Abrupt Motions , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Gérard G. Medioni,et al.  Tracking many vehicles in wide area aerial surveillance , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[12]  Bing Chen,et al.  Object tracking with particle filter in UAV video , 2013, Other Conferences.

[13]  Y. Zhang,et al.  Design of Dual Field of View and Zoom Infrared Optical System , 2011 .

[14]  Harpreet S. Sawhney,et al.  Vehicle detection and tracking in wide field-of-view aerial video , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Isabella Szottka,et al.  Advanced Particle Filtering for Airborne Vehicle Tracking in Urban Areas , 2014, IEEE Geoscience and Remote Sensing Letters.

[16]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Riccardo Leonardi,et al.  Scene break detection: a comparison , 1998, Proceedings Eighth International Workshop on Research Issues in Data Engineering. Continuous-Media Databases and Applications.

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

[19]  Gérard G. Medioni,et al.  Persistent Tracking for Wide Area Aerial Surveillance , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Anton van den Hengel,et al.  Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking , 2007, IEEE Transactions on Image Processing.

[21]  Isaac Kaminer,et al.  Autonomous feature following for visual surveillance using a small unmanned aerial vehicle with gimbaled camera system , 2010 .

[22]  Elder M. Hemerly,et al.  Automatic Georeferencing of Images Acquired by UAV’s , 2014, Int. J. Autom. Comput..