Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

In this paper, by analyzing the characteristics of infrared moving targets, a Symmetric Frame Differencing Target Detection algorithm based on local clustering segmentation is proposed. In consideration of the high real-time performance and accuracy of traditional symmetric differencing, this novel algorithm uses local grayscale clustering to accomplish target detection after carrying out symmetric frame differencing to locate the regions of change. In addition, the mean shift tracking algorithm is also improved to solve the problem of missed targets caused by error convergence. As a result, a kernel-based mean shift target tracking algorithm based on detection updates is also proposed. This tracking algorithm makes use of the interaction between detection and tracking to correct the tracking errors in real time and to realize robust target tracking in complex scenes. In addition, the validity, robustness and stability of the proposed algorithms are all verified by experiments on mid-infrared aerial sequences with vehicles as targets.

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

[2]  Chun-Ming Tsai,et al.  Intelligent Moving Objects Detection via Adaptive Frame Differencing Method , 2013, ACIIDS.

[3]  Mubarak Shah,et al.  Target tracking in airborne forward looking infrared imagery , 2003, Image Vis. Comput..

[4]  Xiaoqin Zhang,et al.  Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions , 2013, IEEE Transactions on Image Processing.

[5]  Vance Faber,et al.  Clustering and the continuous k-means algorithm , 1994 .

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

[7]  M. Shah,et al.  Moving Object Detection and Tracking in Forward Looking Infra-Red Aerial Imagery , 2011 .

[8]  Antoine Vacavant,et al.  A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos , 2014, Comput. Vis. Image Underst..

[9]  Alberto Del Bimbo,et al.  Object Tracking by Oversampling Local Features , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Han Jian-yu.  Moving Object Detection and Tracking Based on Weighted Accumulative Difference , 2009 .

[11]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[12]  Hichem Snoussi,et al.  Improved mean shift integrating texture and color features for robust real time object tracking , 2012, The Visual Computer.

[13]  Peter Reinartz,et al.  An Operational System for Estimating Road Traffic Information from Aerial Images , 2014, Remote. Sens..

[14]  Aristidis Likas,et al.  Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering , 2011, Image Vis. Comput..

[15]  Ramesh C. Jain,et al.  On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  N. Malmurugan,et al.  OFGM-SMED: An Efficient and Robust Foreground Object Detection in Compressed Video Sequences , 2014, Eng. Appl. Artif. Intell..

[17]  Jie Yang,et al.  Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure. , 2007, Applied optics.

[18]  辛云宏 Xin Yun-hong,et al.  Combined Continuous Frame Difference with Background Difference Method for Moving Object Detection , 2014 .

[19]  Jiri Matas,et al.  Online learning of robust object detectors during unstable tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[20]  Marco Diani,et al.  Comparative analysis of clutter removal techniques over experimental IR images , 2005 .

[21]  Zhang Xiang-jin.  IR Motive Detection Using Image Subtraction and Optical Flow , 2012 .

[22]  Fredrik Gustafsson,et al.  Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle , 2012, Remote. Sens..

[23]  Hassan Ghassemian,et al.  Deformable Contour-Based Maneuvering Flying Vehicle Tracking in Color Video Sequences , 2013 .

[24]  Jie Hou,et al.  A self-adaptive optical flow method for the moving object detection in the video sequences , 2014 .

[25]  Ehud Rivlin,et al.  Mean Shift tracking with multiple reference color histograms , 2010, Comput. Vis. Image Underst..

[26]  Jiri Matas,et al.  Face-TLD: Tracking-Learning-Detection applied to faces , 2010, 2010 IEEE International Conference on Image Processing.

[27]  David Rees,et al.  Bayesian fusion of thermal and visible spectra camera data for region based tracking with rapid background adaptation , 2012, 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[28]  Jenq-Neng Hwang,et al.  Vehicle tracking iterative by Kalman-based constrained multiple-kernel and 3-D model-based localization , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[29]  Qiu Guangnen.  Moving object detection algorithm based on symmetrical-differencing and background subtraction , 2014 .

[30]  Ehud Rivlin,et al.  Tracking by Affine Kernel Transformations Using Color and Boundary Cues , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  LU Li-hong,et al.  Improved mean-shift based IR target tracking algorithm , 2010 .

[32]  Azeddine Beghdadi,et al.  On the analysis of background subtraction techniques using Gaussian Mixture Models , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[33]  Amitava Das,et al.  Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences , 2014 .

[34]  Wangsheng Yu,et al.  Region edge histogram: A new feature for region-based tracking , 2014, 2014 12th International Conference on Signal Processing (ICSP).

[35]  Manish Khare,et al.  Single change detection-based moving object segmentation by using Daubechies complex wavelet transform , 2014, IET Image Process..

[36]  K. Moffett,et al.  Remote Sens , 2015 .

[37]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  A. Mitiche,et al.  Model-Free, Occlusion Accommodating Active Contour Tracking , 2012 .

[39]  Guoliang Fan,et al.  Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set , 2014, Sensors.