# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Ground Truth**} & \multicolumn{2}{c}{**Proposed Method**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-5}  & **Up** & **Down** & **Up** & **Down** & \\ \hline Video1 & 9 & 12 & 12 & 13 & 80.95 \\ Video2 & 5 & 9 & 15 & 9 & 28.57 \\ Video3 & 8 & 11 & 7 & 11 & 94.74 \\ Video4 & 14 & 40 & 13 & 39 & 96.30 \\ Video5 & 5 & 13 & 6 & 13 & 94.44 \\ Video6 & 8 & 22 & 10 & 20 & 100.00 \\ Video7 & 26 & 9 & 25 & 9 & 97.14 \\ Video8 & 21 & 23 & 20 & 22 & 95.45 \\ Video9 & 14 & 15 & 14 & 15 & 100.00 \\ Video10 & 16 & 10 & 16 & 10 & 100.00 \\ Video11 & 29 & 6 & 27 & 6 & 94.29 \\ Video12 & 28 & 11 & 24 & 11 & 89.74 \\ Video13 & 22 & 19 & 25 & 18 & 95.12 \\ Video14 & 21 & 15 & 21 & 14 & 97.22 \\ Video15 & 6 & 3 & 4 & 4 & 88.89 \\ Video16 & 4 & 13 & 4 & 13 & 100.00 \\ Video17 & 11 & 15 & 11 & 14 & 96.15 \\ Video18 & 14 & 7 & 12 & 7 & 90.48 \\ Video19 & 9 & 11 & 8 & 11 & 95.00 \\ Video20 & 7 & 30 & 9 & 29 & 97.30 \\ Video21 & 16 & 15 & 15 & 13 & 90.32 \\ Video22 & 5 & 15 & 5 & 16 & 95.00 \\ Video23 & 14 & 25 & 12 & 26 & 97.44 \\ \hline Sum & 312 & 349 & 315 & 343 & 98.95\% \\ \hline Total & \multicolumn{2}{c}{661} & \multicolumn{2}{c}{658} & \multicolumn{2}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: People counting accuracy of the proposed method. results showed that the average counting accuracy of the two techniques was 79.6%, while the accuracy of the proposed technique was 98.9%. The two techniques using deep learning had the problem that the tracker could not track the object to the end when occlusion occurred. On the other hand, the proposed technique solved the occlusion problem by projecting the side view onto the top view. The reason for the poor performance of the proposed method in VGA resolution was that the lower the resolution, the more the stereo matching could not express the long distance. Table 4 shows the Frame Per Second (FPS) and resource occupancy per resolution of the proposed method on the NVIDIA Jetson TX2. The QVGA and VGA resolutions were operated only on the CPU due to the small amount of computation. Since HD and FHD resolution have a large amount of computation, it cannot be operated in real time by using the CPU alone. Therefore, we used CUDA, a parallel processing program using a GPU. GPU occupancy was measured using the tegrastats application provided by the Jetson TX2. The experimental results showed that the QVGA and VGA resolutions were 15.4 FPS and 9.6 FPS, respectively. In general, in the field of surveillance cameras, the fact that an algorithm operates in real time means that it operates above 10 FPS. Therefore, the proposed method worked in real time in the QVGA, VGA, and HD resolutions. On the other hand, the FHD resolution worked at 5.4 FPS even though GPU was used. The portion that occupied most of the computation amount was stereo matching. In general, however, most applications did not require stereo matching at FHD resolution. In the QVGA and VGA resolutions, the CPU occupied about 25% and the HD and FHD resolutions took up about 20%. The proposed method did not require a high occupancy, so there were enough resources to port other applications in the future.