## 2 Detection Model
### Foreground Motion Blocks Extraction
#### 2.1.1 Sampling Areas Determination
Then, foreground moving objects are identified by excluding global motion vectors. They also introduced a statistic variable named fourth-order moment to distinguish the background signal and moving objects. Recently, [PERSON] et al. [73] propose a hybrid tracking method which detects moving objects in videos compressed according to H.265/HEVC standard. They introduced Markov Random Field (MRF) model to captures spatial and temporal coherence of the moving object. Also, background/foreground color modeling is proposed both for I frames and for P frames. Figure 14 displays some visual effects comparisons among those previous works corresponding with motion vector extraction methods. we have selected five sequences for performance tests. These sequences contain both big or small objects (people) with various background changes. Three methods are introduced for comparison purposes; they are [PERSON] et al. [69], [PERSON] et al. [72] and [PERSON] et al. [73]. As seen in Figure 14, the proposed method meets a satiable results that clearly reflects the details of the moving objects. For detection accuracy analysis, we plot the presion curves

Figure 13: Running time evaluation for several ROI detection approaches. (**a**) Time consumption varies from different sequences. (**b**) Time consumption varies from different resolution. among the four tested methods. This results are displayed in Figure 15, one can see that the proposed method can meet the robustness demands for different test cases. Also, the detection precison is more stable than others among the selected test sequences. Figure 14: Visual effect comparisons among various motion vector extraction method (**a**) Original sequences (from top to bottom: _Pedestrians_ #3, _Pedestrians_ #15, _[PERSON]_ #31, _Office_ #18, _[PERSON]_ #10, _Party_ #27 ). (**b**) [PERSON] et al. [69]. (**c**) [PERSON] et al. [72]. (**d**) [PERSON] et al. [73]. (**e**) Our method. Figure 15: _Cont._

## 4 Conclusions

In this paper, we design a new foreground ROI detection method for video sequences based on two ROI models: \"inter\" and \"intra\". The \"inter\" model is in charge of acquiring the real motion blocks via global motion detection and compensation. Several detection metrics and a decision tree model are proposed in the inter model, including camera status decision, motion vector subtraction. Also, different categories of camera statuses are taken into consideration with company of global motion compensation. The \"intra\" model is in charge of revealing the object details by the object filtering approach, and it extracts both the edge and inner part of objects in the frame. Notice that ROI detection focuses on moving details of objects. The output of the \"inter\" model gives the motion information however not specific which is represented as blocks, while the \"intra\" model uses this motion information as the pilot and it reconstructs the output by image segmentation and combination using the proposed cluster approach together with a motion area pruning algorithm. Experimental results demonstrate that the proposed method gets a better result both on detection precision and on computational time. Future works will include machine learning approaches to detect the actions of ROI objects and improves the detection quality specifically in the action areas, and the parameters of the proposed method will be optimized which suits video with various characteristics. Conceptualization, Z.Z. ; Methodology, Z.Z. and T.J.; Software, B.D. ; Visualization, M.G. ; Writing-review & editing, X.L. This research received no external funding. The authors declare no conflict of interest. ## References

* [PERSON] (1979) [PERSON] Digital color image processing within the framework of a human visual model. _IEEE Trans. Acoust. Speech Signal Process._ **1979**, _27_, 380-393. [CrossRef]
* [PERSON] and [PERSON] (2004) [PERSON]; [PERSON] A region-based H.263+ codec and its rate control for low VBR video. _IEEE Trans. Multimed._**2004**, \(6\), 489-500.