Tracking Multiple Objects with Locally Adaptive Generalized Optimum Correlation Filters

—An algorithm for tracking multiple objects using locally adaptive generalized filtering is proposed. The tracking algorithm is invariant to geometric transformations of objects, partial occlusion of objects, nonuniform illumination of scene, and additive noise in scene images. The proposed system u...

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Bibliographic Details
Published in:Journal of communications technology & electronics 2020-06, Vol.65 (6), p.716-724
Main Authors: Kober, V. I., Ruchay, A. N., Karnaukhov, V. N.
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithms
Communications Engineering
Comparative analysis
Engineering
Geometric transformation
Iterative methods
Networks
Occlusion
Optical tracking
Optimization
Theory and Methods of Information Processing
Training
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Summary:—An algorithm for tracking multiple objects using locally adaptive generalized filtering is proposed. The tracking algorithm is invariant to geometric transformations of objects, partial occlusion of objects, nonuniform illumination of scene, and additive noise in scene images. The proposed system utilizes generalized optimal correlation filters and a prediction scheme based on the kinematic model of objects motion. With the help of iterative training, the training system can be adapted to current scene changes. The performance of the proposed algorithm is compared with that of the state-of-the-art visual tracking algorithms on known databases in terms of commonly accepted quality metrics and processing time.
ISSN:1064-2269
1555-6557
DOI:10.1134/S1064226920060169