# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 3 Experimental Results  
The stereo camera used in the experiment was a LI-JETSON-KIT-IMX185 CS-D [29] released by Leopard imaging. It consisted of two IMX 185 image sensors from Sony and an adapter board for synchronizing the cameras. The IMX 185 and its board are connected by a 30 cm long I-PEX cable [19]. The board is only available in the Jetson TX series and could be connected to the camera input of the Jetson TX2 [11] to acquire real-time synchronized stereo images. Table 1 shows the specifications of the IMX 185 sensor. To measure the people-counting performance of the proposed method, the camera was installed in a place with a lot of floating population. The experimental sequence was in HD resolution, consisting of 23 videos in five minutes. The counting line was set on a major route that passed a lot of people, as shown in Figure 6. Updating the counting number every time a person crossed the counting line caused the counting number to increase abnormally, for example, a person hanging around near a \begin{table}
\begin{tabular}{c} Sony Diagonal 8.58 mm Type 1/1.9 Complementary Metal Oxide Semiconductor (CMOS) \\ Image Sensor IMX185 LQJ \\ Maximum 1937 \(\times\) 1217 \\ 3.75 \(\upmu\)m \(\times\) 3.75 \(\upmu\)m \\ Color sensor \\ Mobile Industry Processor Interface (MIPI) output \\ 38 mm \(\times\) 38 mm \\ 56 g \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IMX 185 specification. Figure 10: Two cases where the trajectory of the object and the counting line intersect: (**a**) Case 1; and (**b**) Case 2.  
counting line. Therefore, we set a benchmark to count the first time a person crossed a counting line and created a ground truth. Table 2 shows the counting accuracy of the proposed method. Up can be seen in Figure 10 as when a person crossed up over the counting line and down was when a person crossed down the counting line. The accuracy is measured using Equation (9):  
\[\frac{|Up_{GT}-Up_{m}|+|Down_{GT}-Down_{m}|}{Up_{GT}+Down_{GT}} \tag{9}\]  
where \(GT\) means ground truth and \(m\) means the value measured in the proposed method. More than 95% accuracy was measured in most of the experimental sequences, and the overall accuracy was measured as 98.95%. In the second experimental sequence, a very low accuracy of 28.57% was measured. In this sequence, a person held a large load and hung around near the counting line. Since the proposed method detects a person using the height of the object, the load was detected as a person, which was the cause of the resulting low accuracy. Table 3 shows the results of a comparison between the proposed method and the deep learning-based methods. The experiments were performed on the experimental sequences used in Table 2. For each method, the accuracy of the measured values against the up and down ground truths of the entire sequence was obtained through Equation (3). GoogleNet-Single Shot Detector (SSD) and MobileNet-SSD were trained with GoogleNet [30] and MobileNet [31], respectively, and then used SSD [32] to detect people. The detected objects were tracked and counted using a Kernelized Correlation Filter (KCF) tracker. The KCF tracker is more accurate than the Kalman tracker as it uses the correlation information of the detected object. Since methods using deep learning detect objects in the side view, using a KCF tracker [33] can obtain a higher accuracy. Nevertheless, the experimental