## 1 Introduction
### Overview and Contributions  
According to the related research listed, a motivation is to propose a method which suits video codecs like HEVC/VPX. In this paper, a new ROI modeling-based detection scheme is designed which copes with video compression standards by proposing two ROI models: \"inter\" and \"intra\". The \"inter\" part includes camera status detection and global motion compensation (GMC). The \"intra\" part includes image segmentation and objects filtering. At last, the detection results are combined together based on the output of two models. Please note that each model can only observe a partial information: The \"inter\" corresponds with motion information and the \"intra\" with objects information, as depicted in Figure 1. Previous works [57; 58; 59; 60] introduce an adaptive feature-based methods (FBMs) which estimates the camera motion with error-least matching points assisted by the Single Homographic (SH) matrix. However, minimizing the movement errors of matching points brings computational costs that reduce the speed in real-time systems. To overcome this disadvantage, we propose a fast and approximate global motion compensation way which uses a flexible sampling set and a decision tree model for classification in the \"inter\" model. The motion vector blocks for objects are acquired via global motion compensation techniques. As for the \"intra\" model, a Kernel Fuzzy-C Means image segmentation approach is proposed to cluster pixels into specific groups. Then, we use a new motion area pruning method to eliminate the \"noise\" motion blocks extracted through the \"inter\" model. Please note that the purpose of \"intra\" model is to cluster objects by colorspaces and combine them with the motion information separately owing to the connectivity correlation of each cluster.  
The contributions of this paper are illustrated below: First, we address the ROI detection problem by designing the \"inter\" and \"intra\" models. In the inter model, we first propose a sampling criteria which accurately samples the background motion blocks. Then, a fast and approximate way for global motion detection and compensation techniques based on the camera status and the decision tree model are proposed. In the intra model, a new adaptive and accurate image segmentation method is designed for clustering foreground objects. We innovate combining two models as the finial detection results by developing a combination algorithm based on color correlation and distance. A motion area pruning algorithm is proposed to eliminate noise motion blocks extracted via GMC. Besides, we test our method on different open datasets. Performance evaluations on both the change detection dataset [61] and official HEVC/AVC test sequences [62] shows our method performs well in terms of detection precision and computing time. The rest of this paper is organized as follows. In Section 2, we elaborate the details of the two proposed models of ROI and the detection algorithm. In Section 3, experimental results are displayed with sufficient analysis for visual effect comparisons and performance. Finally, conclusion is drawn in Section 4.  
Figure 1: Basic structure of ROI Detection model.