# FenceTalk: Exploring False Negatives in Moving Object Detection
## 3 FenceTalk Architecture  
FenceTalk utilizes a network camera to transmit real-time images for image recognition. Figure 2 illustrates the FenceTalk architecture, comprising six key components. At its core is the FenceTalk server, which includes the FenceTalk engine (Figure 2 (1)), derived from IoTtalk [6], and a developer GUI (Figure 2 (2)). Following the IoTtalk philosophy, the FenceTalk server operates as an IoT server and employs an image database known as FenceTalk DataBase (FTDB) to store images extracted from the video streams (Figure 2 (3)). Additionally, the remaining four components within FenceTalk serve as IoT devices, encompassing the camera software module (Figure 2 (4)), the Yolo module (Figure 2 (5)), the SSIM module (Figure 2 (6)), and the user GUI module (Figure 2 (7)). Each of these software modules consists of two parts: device application (DA) and sensor and actuator application (SA). The DA is responsible for communication with the FenceTalk server using HTTP communication ((5)\(\leftrightarrow\)(1), (6)\(\leftrightarrow\)(1), (7)\(\leftrightarrow\)(1) in Figure 2), which can be achieved through wired means (e.g., Ethernet) or wireless options (e.g., 5G or Wi-Fi) for communication. The SA part implements functionalities related to IoT devices, such as Yolo SA for object detection based on the Yolo model and SSIM SA for detecting moving objects based on SSIM. Figure 2: The FenceTalk architecture. The network camera (Figure 2 (4)) uses the real-time streaming protocol (RTSP) to stream the image frames. If the user draws a red polygon to define a fenced area within the desired field, FenceTalk will exclusively perform object detection within this specified area. The Yolo SA (Figure 2 (5)) continuously receives the latest streaming image frames and conducts object detection within the designated region using the Yolov4-tiny model. The results of the detection process, including object names, positions, and image paths, are stored in FTDB (Figure 2 (3)). The ongoing Yolo recognition result is transmitted to the FenceTalk Engine (Figure 2 (1)) through the DA interface Yolo-I. The engine receives this data and has the capability to preprocess it using custom functions before transmitting it to connected IoT devices. Specifically, the engine forwards the Yolo recognition results to SSIM (Figure 2 (6)) through the DA interface SSIM-O for further evaluation. The outcomes of this evaluation, which include information about the presence of moving objects and corresponding image paths, are then stored in FTDB. Simultaneously, the SSIM assessment result is sent to the FenceTalk server via the DA interface SSIM-I, which is sent back to the Yolo module for further processing (See Section 4). The results of object recognition are subsequently displayed on user-defined devices (Figure 2 (7)). In FenceTalk, IoT devices can be effortlessly connected and configured using the developer GUI (Figure 2 (2)) accessible through a web browser. Figure 3 depicts this GUI, wherein the involved IoT devices can be chosen from the model pulldown list (Figure 3 (1)). When we opt for the Yolo IoT device (i.e., the Yolo module), two icons are displayed within the GUI window. The icon on the left side of the window indicates the DA interfaces Yolo-I and Msg-I (Figure 3 (2)). The icon on the right side represents the DA interface Yolo-O (Figure 3 (3)). Similarly, we can choose the SSIM and the display devices. To establish connections between the IoT devices, all that is required is dragging the \"join links.\" For instance, Join 1 connects Yolo-I to SSIM-O. This link creates an automatic communication pathway between the Yolo module and the SSIM module. Consequently, the configuration shown in Figure 3 yields the FenceTalk architecture displayed in Figure 2.  
images is typically performed manually. Unfortunately, our experience indicates that the size of the non-moving object database is usually substantial, making the identification of false positive images a highly tedious task.