Object Detection by Keygraph Classification
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
Resolving partial occlusions in crowded environments utilizing range data and video cameras
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Discriminative nonorthogonal binary subspace tracking
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
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Two major problems for model-based object tracking are: 1) how to represent an object so that it can effectively be discriminated with background and other objects; 2) how to dynamically update the model to accommodate the object appearance and structure changes. Traditional appearance based representations (like color histogram) fails when the object has rich texture. In this paper, we present a novel feature based object representation attributed relational graph (ARG) for reliable object tracking. The object is modeled with invariant features (SIFT) and their relationship is encoded in the form of an ARG that can effectively distinguish itself from background and other objects. We adopt a competitive and efficient dynamic model to adoptively update the object model by adding new stable features as well as deleting inactive features. A relaxation labeling method is used to match the model graph with the observation to gel the best object position. Experiments show that our method can get reliable track even under dramatic appearance changes, occlusions, etc.