Video-Based Automatic Target Recognition

Abstract : 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]  Wei Wu,et al.  A method of vehicle classification using models and neural networks , 2001, IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings (Cat. No.01CH37202).

[2]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[3]  Michael I. Jordan,et al.  Mixtures of Probabilistic Principal Component Analyzers , 2001 .

[4]  Dieter Koller,et al.  Moving Object Recognition and Classification based on Recursive Shape Parameter Estimation , 1993, CVPR 1993.

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

[6]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

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

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

[9]  Osama Masoud,et al.  Detection and classification of vehicles , 2002, IEEE Trans. Intell. Transp. Syst..