###### Abstract  
Traditional video object segmentation often has low detection speed and inaccurate results due to the jitter caused by the pan-and-tilt or hand-held devices. Deep neural network (DNN) has been widely adopted to address these problems; however, it relies on a large number of annotated data and high-performance computing units. Therefore, DNN is not suitable for some special scenarios (e.g., no prior knowledge or powerful computing ability). In this paper, we propose RoiSeg, an effective moving object segmentation approach based on Region-of-Interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. Specifically, we first hypothesize that the central n \(\times\) n pixels of images act as the ROI to represent the features of the segmented moving object. Second, we pool the ROI to a central point of the foreground to simplify the segmentation problem into a classification problem based on ROI. Third but not the least, we implement a trajectory-based classifier and an online updating mechanism to address the classification problem and the compensation of class imbalance, respectively. We conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms. Moreover, RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes.  
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Footnote †: journal: Applied sciences  
To deal with these challenges, we propose RoiSeg, an effective object segmentation approach based on Region-of-interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. RoiSeg hypothesizes the central n*n pixels of images as the ROI to reflect the features of moving object, then the classification of all pixels is turned into that of ROI central points. In the field of classification, the supervised learning methods usually provide a better accuracy compared with the unsupervised learning methods, however, they inevitably need more annotated datasets, hence increasing the workload of computing units [7]. To address this problem, RoiSeg adopts an automatic generation method based on ROI to produce the training samples with the unsupervised learning method. Moreover, RoiSeg also implements an online sample classifier to compensate the imbalance of different classes. We highlight our main contributions as follows:  
* We propose RoiSeg, an effective object segmentation approach based on ROI, which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes.
* We hypothesize the central n*n pixels as the ROI and simplify the foreground segmentation into a classification problem based on ROI. In addition, we propose an automatic generation method to produce the training samples and implement an online sample classifier to compensate the imbalance of different classes, respectively.
* We also conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms.  
The rest of this paper is organized as follows. Section 2 presents a review of related works. The description of RoiSeg is demonstrated in Section 3. The comparison experiments are given in Section 4. Finally, the conclusion is drawn in Section 5.