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Identification and tracking of dynamic 3D objects from Synthetic Aperture Radar (SAR) and Infrared (IR) Thermal imaging in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we primarily present an approach for 3D objects recognition and tracking based on their multi-modality (e.g., SAR and IR) imagery signatures and discuss a multi-scale scheme for multi-modality imagery salient keypoint descriptors extraction from 3D objects. Next, we describe how to cluster local salient keypoints and model them as signature surface patch features suitable for object detection and recognition. During our supervised training phase, multiple views of test model are presented to the system where a set of multi-scale invariant surface features are extracted from each model and registered as the object’s class signature exemplar. These features are employed during the online recognition phase to generate recognition hypotheses. When each object of interest is verified and recognized, the object’s attributes are annotated semantically. The coded semantic annotations are then efficiently presented to a Hidden Markov Model (HMM) for spatiotemporal object state discovery and tracking. Through this process, corresponding features of same objects from multiple sequential multi-modality imagery data are realized and tracked overtime. The proposed algorithm was tested using IRIS simulation model where two test scenarios were constructed. One scenario is used for activity recognition of ground-based vehicles, and the other one is used for classification of Unmanned Aerial Vehicles (UAV’s). In both scenarios, synthetic SAR and IR imagery are generated using IRIS simulation model for the purpose of training and testing of newly developed algorithms. Experimental results show that our algorithms offer significant efficiency and effectiveness.
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