# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 1 Introduction  
Surveillance cameras play an important role in the development of industry and security in modern society such as in crime prevention, arresting criminals, and collecting large amounts of data for marketing purposes. The surveillance camera industry is not only growing quantitatively, but is also growing qualitatively such as through increasing camera resolution and shooting distance. In recent years, the intelligent surveillance camera industry has attracted much attention in the product intelligence trend, which is one of the keywords of the fourth industrial revolution. An intelligent surveillance camera not only captures images, but these cameras also include automation technology that allows the camera to determine the identity of a person or the number of people. With the development of intelligent surveillance camera technology, the reliance on human resources for image analysis has been greatly reduced. People-counting technology is a technique that analyzes images to determine how many people have crossed the counting line [1]. The results of this technology can be used as important data to understand the conversion rate of purchases, analysis of visitors, and the movement of visitors to operate stores and large shopping malls. As long as the history of surveillance cameras is old, there have been various people-counting methods. [PERSON] [2] used Histogram of Oriented Gradients (HoG) [3] to detect head-shoulder and Principal Component Analysis (PCA) [4] to improve detection performance. [PERSON] [5] detected a person using You Only Look Once--People Counting (YOLO-PC), which extended the original YOLO [6] by using a deep-running approach to obtain more accurate people counting. [PERSON] [7] used two Impulse Radio Ultra-Wideband (IR-UWB) radars to detect a person's direction of movement. However, since the above methods are designed based on a mono camera, the detectionrate is lower when people are occluded by people. The Red-Green-Blue (RGB)-based method is also sensitive to illumination changes and shadows. One way to overcome these challenges is to use depth image. [PERSON] [8] defined human characteristics in a depth image using a single RGB-Depth (RGB-D) camera and then detected a person through supervised learning. [PERSON] [9] detected a person by using a stereo thermal camera to reconstruct the image in three dimensions. In this surveillance camera, the depth information could improve the performance when compared to the method using the mono camera by estimating not only the human silhouette information, but also the spatial structure around the camera. However, this method could not be practically operated due to the limitation of the hardware performance of the existing camera platform [10]. Recently, a high-performance embedded board with a Graphics Processing Unit (GPU) has been introduced, and these products have made it possible to substantially utilize algorithms with high computational complexity. Among them, the Jetson TX2 [11], launched by NVIDIA in CA, USA in 2017, was developed to run AI algorithms on embedded boards. This product had a Central Processing Unit (CPU) with a total of six cores, and a GPU with 256 cores was installed, enabling the parallel processing of algorithms using Compute Unified Device Architecture (CUDA). The surveillance camera is divided into the side view and top view depending on the installation angle. The camera angle of the side view is diagonal and the angle of the top view is vertical. Since the depth image of the top-view is the closest distance between the head and the camera, the pixel corresponding to the head has the lowest value [12]. This feature facilitates people detection, and many people-counting products have been released. On the other hand, the side view cannot detect a person only by the top-view feature because the depth value differs according to the distance between the camera and the object. In this paper, we proposed a people-counting system using a stereo camera from the side view.