Color object segmentation and tracking using flexible statistical model and level-set
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Color object segmentation and tracking using flexible statistical model and level-set Sami Bourouis1,2 · Ines Channoufi2 · Roobaea Alroobaea1 Murad Andejany3 · Nizar Bouguila4
· Saeed Rubaiee3
·
Received: 6 April 2020 / Revised: 22 July 2020 / Accepted: 2 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with a post processing step implemented with a variational active contour. Flexible learning method of a finite mixture of bounded generalized Gaussian distributions using the Minimum Message Length (MML) principle is developed to cope with the complexity of color images modeling. We deal here simultaneously with the issues of data-model fitting, determining automatically the optimal number of classes and selecting relevant features. Indeed, a feature selection step based on MML is implemented to eliminate uninformative features and therefore improving the algorithm’s performance. For model’s parameters estimation, the maximum likelihood (ML) was investigated and conducted via expectation maximization (EM) algorithm. The obtained object boundaries in the first step are tracked on each frame of a given sequence using a geometric level-set approach. The implementation has the advantage to help in improving the computational efficiency in high-dimensional spaces. We demonstrate the effectiveness of the developed segmentation method through several experiments. Obtained results reveal that our approach is able to achieve higher precision as compared to several other methods for color image segmentation and object tracking. Keywords Color image segmentation · Object tracking · Mixture bounded model · Feature selection · Minimum message length · Level-set
1 Introduction One of the most challenging problems in pattern recognition and image processing applications is object detection and tracking. Object detection and tracking is now widely used in video surveillance systems, smart vehicles, traffic monitoring and human computer interaction. To deal with this problem, we always proceed to involve more visual information Sami Bourouis
[email protected]
Extended author information available on the last page of the article.
Multimedia Tools and Applications
(characteristics) into the segmentation algorithm. Visual pixel’s characteristics can be either informative or uninformative. In case of presence of uninformative information (like noise), the object detection process will be complex and extremely time consuming. Also, the presence of nonuniform illumination or self-shadow can generate false clusters and therefore all these matters may easily conduct to over-segmentation. On the other hand, taking into account all possible visual features (color, texture, shape, etc.) may decrease the algorithm’s performance as cited in [17, 28, 29, 37, 41, 43, 54, 57, 58, 61]. For all these reasons, it is better to not consider
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