Integrated Detection, Tracking and Recognition for IR Video-Based Vehicle Classification

We present an approach for vehicle classification in IR video sequences by integrating detection, tracking and recognition. The method has two steps. First, the moving target is automatically detected using a detection algorithm. Next, we perform simultaneous tracking and recognition using an appearance-model based particle filter. The tracking result is evaluated at each frame. Low confidence in tracking performance initiates a new cycle of detection, tracking and classification. We demonstrate the robustness of the proposed method using outdoor IR video sequences

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

[2]  Vladimir Petrovic,et al.  Vehicle type recognition with match refinement , 2004, ICPR 2004.

[3]  Katsushi Ikeuchi,et al.  Recognizing vehicle in infra-red images using IMAP parallel vision board , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[4]  Ming-Hsuan Yang,et al.  Adaptive Probabilistic Visual Tracking with Incremental Subspace Update , 2004, ECCV.

[5]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[6]  Tieniu Tan,et al.  Model-Based Localisation and Recognition of Road Vehicles , 1998, International Journal of Computer Vision.

[7]  David J. Kriegman,et al.  Online learning of probabilistic appearance manifolds for video-based recognition and tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[9]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Anil K. Jain,et al.  Vehicle Segmentation and Classification Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Rama Chellappa,et al.  Intra-personal Kernel Space for Face Recognition Intra-personal Kernel Space for Face Recognition , 2004 .

[13]  Yan Zhang,et al.  On the Euclidean distance of images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Toshimitsu Kaneko,et al.  Feature selection for reliable tracking using template matching , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  W. Eric L. Grimson,et al.  Edge-based rich representation for vehicle classification , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[16]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[17]  Christopher M. Bishop,et al.  Mixtures of Probabilistic Principal Component Analyzers , 1999, Neural Computation.

[18]  Richard Bowden,et al.  Simultaneous modeling and tracking (SMAT) of feature sets , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Ehud Rivlin,et al.  A probabilistic framework for combining tracking algorithms , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[20]  Takahiro Ishikawa,et al.  The template update problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Katsushi Ikeuchi,et al.  Recognizing vehicles in infrared images using IMAP parallel vision board , 2001, IEEE Trans. Intell. Transp. Syst..