# 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
However, when the speed of the human was fast as in case of running or the camera was at a far distance, the MV's density decreased, and the detector returned lower average IoU scores. For object tracking evaluation, the scenarios were run multiple times with different detection score thresholds \(\alpha\) of 0.25, 0.4, and 0.5 with the results shown in Figure 14.

In this experiment, each \(\alpha\) threshold was applied for the same three scenarios of human walking in Figure 14a,d,g, human running in Figure 14b,e,h, and the far camera distance in Figure 14c,f,i. We see that with each test scenario, with a lower \(\alpha\) threshold value, the tracking object capability was better with longer object trajectories. However, it will increase the number of false alarm detections if the noisy MVs appear frequently in some specified areas. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Video Test** \\ **Sequence** \\ \end{tabular} }} & \multicolumn{2}{c}{**Scenario**} & \multirow{2}{*}{**IoU Average**} \\ \cline{2-2} \cline{4-4}  & **Camera Position** & & **Moving Speed** \\ \hline Our recorded test video & Near & Normal & 0.75 \\ Our recorded test video & Near & Fast & 0.26 \\ Video test from VIRAT & Far & Normal & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average IoU of the moving object detection in the compressed-domain in different scenarios. Figure 13: Moving object detection by the proposed method in different scenarios: (**a–d**) human walking; (**e–h**) human running; (**i–l**) test with a far distance of the camera. To optimize the running time speed, as well as the performance, the edge node and cloud node were all implemented in C++ using the multithread architecture. We observed that the proposed method does not indicate additional computational difficulty and achieves the approved evaluated results in terms of processing time when compared with previous studies [15; 16]. The average time consumption is measured with different video resolutions and shown in Table 5. Compare to other studies, the average processing time for high definition resolution video is approximately 39 ms/frame, which outperforms most of the state-of-the-art methods. This indicates that the proposed algorithm almost handles the data in real-time. ### Performance Evaluation Results

In this experiment, the proposed edge-to-cloud platform will be implemented and evaluated. Our recorded video test sequence is selected because it includes different moving object speeds. After experiments using different \(\alpha\) in different scenarios, we found that the optimal \(\alpha\) was 0.5 for this scenario. The entire demonstration was recorded and uploaded [44]. The evaluated performances of the demonstration are presented in detail as follows. Figure 14: Moving object tracking by the proposed method with different \(\alpha\) thresholds: \(\alpha=0.25\) in (**a–c**), \(\alpha=0.4\) in (**d–f**), and \(\alpha=0.5\) in (**g–i**). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Frame Size** & **ST-MRF[16]** & **Graph Cuts[15]** & **Proposed Method** \\ \hline
1280 \(\times\) 720 & 64 ms (16 FPS) & 62 ms (17 FPS) & 39 ms (26 FPS) \\
1920 \(\times\) 1080 & N/A & N/A & 69 ms (14 FPS) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average per-frame running times for the preprocessing and tracking procedures. Values are expressed in milliseconds (ms) and frames per second (FPS). ST-MRF, spatio-temporal Markov random field. ## 5 Conclusions

In this work, we propose an edge computing-based video analytics platform that utilizes the compressed-domain features at the edge device node to minimize the video data transfer of surveillance camera feeds to the video analytics server on the cloud. By exploiting the MVs from the compressed bitstream, edge devices can achieve a low processing complexity in the object-tracking task for surveillance videos.