# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 3 Methodology
### Light-Weight Runtime Moving Object Detection in the Video Compressed-Domain
#### 3.2.2 IoU-Based Moving Objects Tracking
Based on the result of the moving object tracking, the edge device will consider whether the compressed frame should be forwarded to the cloud server. We implemented the proposed method including various computer vision algorithms such as video coding MV extraction, data clustering, and object tracking. The evaluation results show that the proposed method achieved the aims of the study and is fully compatible with cloud-based video analytics for surveillance systems without issues related to delay time or conflicts with other modules. Our implemented application is a light-weight edge-to-cloud platform that can be easily applied to other cloud-based video analytics application. In the future, we will continue to improve the video motion vector analysis method to recognize extremely small motions and reduce noise motions with higher accuracy. Conceptualization and Data curation and Writing--original draft, D.V.N. ; Project administration and Supervision and Writing--review & editing, J.C. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A2C1013308) and the Gachon University research fund of 2019 (Grant No. GCU-2019-0776). The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Computing Resources** & **Conventional Method** & **Proposed Method** & **Performance Ratio** \\ \hline GPU Utilization (\%) & 65.61 & 33.73 & 0.51 \\ CPU Utilization (\%) & 50.24 & 25.9 & 0.51 \\ Download Throughput (Kbps) & 4028 & 1808.2 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average computing resources of both the conventional method and the proposed method. Figure 17: Network download throughput monitoring with both the conventional method and the proposed method. ## References

* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Demo: Video Analytics-Killer App for Edge Computing. In _ACM MobiSys_; Association for Computing Machinery: Seoul, Korea, 2019. * [PERSON] (2020) [PERSON] Video Surveillance Installed Base Report--2019. Available online: [[https://technology.informa.com/607069/video-surveillance-installed-base-report-2019](https://technology.informa.com/607069/video-surveillance-installed-base-report-2019)]([https://technology.informa.com/607069/video-surveillance-installed-base-report-2019](https://technology.informa.com/607069/video-surveillance-installed-base-report-2019)) (accessed on 3 September 2020). * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Towards Scalable Video Analytics at the Edge. In Proceedings of the 2019 16 th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Boston, MA, USA, 10-13 June 2019; pp. 1-9. * _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Moving vehicle detection based on fuzzy background subtraction. In Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 20-24 August 2014; pp. 529-532. * _[PERSON] and [PERSON] (2016)_ [PERSON]; [PERSON] Segmentation of moving objects using background subtraction method in complex environments. _Radioengineering_**2016**, _25_, 399-408. [CrossRef]
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON] Detecting moving object using background subtraction algorithm in FPGA. In Proceedings of the IEEE 2014 Fourth International Conference on Advances in Computing and Communications, Kerala, India, 27-29 August 2014; pp. 117-120. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Ground moving target indication based on optical flow in single-channel SAR. _IEEE Geosci. Remote Sens. Lett._**2019**, _16_, 1051-1055. [CrossRef]
* _[PERSON] et al. (2000)_ [PERSON]; [PERSON]; [PERSON] Object tracking for retrieval applications in MPEG-2. _IEEE Trans. Circuits Syst. Video Technol._**2000**, _10_, 427-432. [CrossRef]
* _[PERSON] et al.