Visual Object Tracking in RGB-D Data via Genetic Feature Learning
| Autor: | Tao Hai, Xian-xian Luo, Mingxin Jiang, Song Yang, Ahmed N. Abdalla, Hai-yan Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Optimization problem
General Computer Science Article Subject Computer science Crossover Population ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Initialization 02 engineering and technology 01 natural sciences lcsh:QA75.5-76.95 Genetic algorithm 0202 electrical engineering electronic engineering information engineering Computer vision education education.field_of_study Multidisciplinary business.industry Deep learning 010401 analytical chemistry 0104 chemical sciences Feature (computer vision) Video tracking RGB color model 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electronic computers. Computer science business Feature learning |
| Zdroj: | Complexity, Vol 2019 (2019) |
| ISSN: | 1099-0526 1076-2787 |
| Popis: | Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed. |
| Databáze: | OpenAIRE |
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