Low resolution vehicle re-identification based on appearance features for wide area motion imagery

The description of vehicle appearance in Wide Area Motion Imagery (WAMI) data is challenging due to low resolution and renunciation of color. However, appearance information can effectively support multiple object tracking or queries in a real-time vehicle database. In this paper, we present a systematic evaluation of existing appearance descriptors that are applicable to low resolution vehicle reidentification in WAMI data. The problem is formulated as a one-to-many re-identification problem in a closed-set, where a query vehicle has to be found in a list of candidates that is ranked w.r.t. their matching similarity. For our evaluation we use a subset of the WPAFB 2009 dataset. Most promising results are achieved by a combined descriptor of Local Binary Patterns (LBP) and Local Variance Measure (VAR) applied to local grid cells of the image. Our results can be used to improve appearance based multiple object tracking algorithms and real-time vehicle database search algorithms.

[1]  M. Keck,et al.  Real-time tracking of low-resolution vehicles for wide-area persistent surveillance , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[2]  Shishir K. Shah,et al.  A survey of approaches and trends in person re-identification , 2014, Image Vis. Comput..

[3]  Shaogang Gong,et al.  Person Re-identification by Video Ranking , 2014, ECCV.

[4]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Ammad Ali,et al.  Face Recognition with Local Binary Patterns , 2012 .

[6]  Guna Seetharaman,et al.  Persistent target tracking using likelihood fusion in wide-area and full motion video sequences , 2012, 2012 15th International Conference on Information Fusion.

[7]  Shaogang Gong,et al.  The Re-identification Challenge , 2014, Person Re-Identification.

[8]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[11]  Raymond N. J. Veldhuis,et al.  Forensic Face Recognition: A Survey , 2010 .

[12]  Anthony Hoogs,et al.  Real-time multi-target tracking at 210 megapixels/second in Wide Area Motion Imagery , 2014, IEEE Winter Conference on Applications of Computer Vision.

[13]  Andrew Zisserman,et al.  Three things everyone should know to improve object retrieval , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Horst Bischof,et al.  Relaxed Pairwise Learned Metric for Person Re-identification , 2012, ECCV.

[15]  Jürgen Beyerer,et al.  A survey on moving object detection for wide area motion imagery , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[17]  Slawomir Bak,et al.  Re-identification by Covariance Descriptors , 2014, Person Re-Identification.

[18]  Shaogang Gong,et al.  Towards Open-World Person Re-Identification by One-Shot Group-Based Verification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  A. G. Amitha Perera,et al.  Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[20]  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.

[21]  Genshe Chen,et al.  Multiway histogram intersection for multi-target tracking , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[22]  Shaogang Gong,et al.  Open-world Person Re-Identification by Multi-Label Assignment Inference , 2014, BMVC.

[23]  XiangTao,et al.  Towards Open-World Person Re-Identification by One-Shot Group-Based Verification , 2016 .

[24]  Shaogang Gong,et al.  Reidentification by Relative Distance Comparison , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Mubarak Shah,et al.  Motion and Appearance Contexts for Tracking and Re-Acquiring Targets in Aerial Videos , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Guna Seetharaman,et al.  Robust Orientation and Appearance Adaptation for Wide-Area Large Format Video Object Tracking , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[27]  Don R. Hush,et al.  Wide-Area Motion Imagery , 2010, IEEE Signal Processing Magazine.

[28]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[29]  Mubarak Shah,et al.  Multiframe Many–Many Point Correspondence for Vehicle Tracking in High Density Wide Area Aerial Videos , 2013, International Journal of Computer Vision.

[30]  Erik Blasch,et al.  Context-driven moving vehicle detection in wide area motion imagery , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[31]  Joseph Catrambone,et al.  A benchmark for vehicle detection on wide area motion imagery , 2015, Defense + Security Symposium.

[32]  Xavier Pennec,et al.  A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.

[33]  M.M. Trivedi,et al.  Video Based Surround Vehicle Detection, Classification and Logging from Moving Platforms: Issues and Approaches , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[34]  Aaron F. Bobick,et al.  Predicting Large Population Data Cumulative Match Characteristic Performance from Small Population Data , 2003, AVBPA.

[35]  Slawomir Bak,et al.  Multiple-shot human re-identification by Mean Riemannian Covariance Grid , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[36]  Xiaogang Wang,et al.  Person Re-identification: System Design and Evaluation Overview , 2014, Person Re-Identification.

[37]  Pascal Fua,et al.  Re-identification for Improved People Tracking , 2014, Person Re-Identification.

[38]  Shaogang Gong,et al.  Person Re-Identification by Support Vector Ranking , 2010, BMVC.

[39]  Riccardo Satta,et al.  Appearance Descriptors for Person Re-identification: a Comprehensive Review , 2013, ArXiv.

[40]  W. Förstner,et al.  A Metric for Covariance Matrices , 2003 .

[41]  Genshe Chen,et al.  Vehicle detection in wide area aerial surveillance using Temporal Context , 2013, Proceedings of the 16th International Conference on Information Fusion.

[42]  Genshe Chen,et al.  Summary of methods in Wide-Area Motion Imagery (WAMI) , 2014, Defense + Security Symposium.

[43]  Rita Cucchiara,et al.  Benchmarking for Person Re-identification , 2014, Person Re-Identification.

[44]  Haroon Idrees,et al.  Detection and Tracking of Large Number of Targets in Wide Area Surveillance , 2010, ECCV.

[45]  Gerard Medioni,et al.  Motion propagation detection association for multi-target tracking in wide area aerial surveillance , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).