# FenceTalk: Exploring False Negatives in Moving Object Detection
## 2 Related Works  
Deep learning models for moving object detection based on CNNs [8; 9; 10; 11] can utilize camera images from specific areas as training data to learn the features of moving objects. This enables accurate identification of moving objects within the frame. In a previous work [4], the authors chose the Yolo model for recognizing humans and vehicles, applying it to a real-time recognition system in an advanced driver assistance system (ADAS). Compared to two-stage recognition models like Faster-RCNN, the one-stage Yolo model strikes a balance between accuracy and speed. Yolo and Yolo-tiny were employed for pedestrian detection, achieving a recall rate of more than 80%. Yolo-tiny, in particular, is well-suited to scenarios requiring real-time detection--aligning with the application context of FenceTalk in our study. We evaluated different versions of YOLO [12; 13]. Despite YOLOv6 [14] having 33% fewer parameters than YOLOv7-tiny and YOLOv4-tiny at the same scale, its inference speed improved by less than 10%. However, this reduction in parameters led to an accuracy drop of nearly 10% compared to YOLOv7-tiny. YOLOv7-tiny's optimization primarily emphasizes training efficiency and inference speed. On our hardware, YOLOv7-tiny demonstrated object detection approximately 10% faster than that of YOLOv4-tiny. Nevertheless, YOLOv7-tiny only achieved an accuracy of 90.86% on our dataset, falling short of YOLOv4-tiny's performance. As a result, we ultimately opted for YOLOv4-tiny as our choice. Now we review the past methods for evaluating image similarity and the applications related to SSIM. Mean square error (MSE) is a common and straightforward approach to measure the similarity between two images. It calculates the mean squared error value of image pixels as an indicator of image similarity. A higher MSE value indicates greater disparity between the two images. The calculation method of MSE considers only the corresponding pixel values of the two images, making its results less reliable. In a study [15], using [PERSON]'s image as an example, JPEG image compression and blurring were applied, resulting in significant differences between the two images. However, their MSE results were similar to the original image. MSE can also exhibit substantial variations in values due to minor pixel rearrangements. The authors of the study slightly shifted and scaled the [PERSON] image, maintaining a similar appearance to the original. Nevertheless, the MSE values for these modified images were significantly high, reaching 871 and 873, respectively, compared to the original image. of MSE. PSNR finds widespread utility in tasks related to image and video compression, restoration, and enhancement, effectively quantifying the extent of information loss during compression or processing. A higher PSNR value indicates greater similarity between the two images. Nevertheless, when it comes to background detection, it does not exhibit the same effectiveness as mixture of Gaussians (MOG) [18] and absolute difference (AD) [19]. MOG [18] is purpose-built for modeling the background within images by employing a mixture of Gaussian distributions. MOG demonstrates adaptability to changing lighting conditions and exhibits robust performance across various scenarios, making it suitable for real-time applications. AD [19] represents another background subtraction technique. While specific implementation details may vary, the core concept is to calculate the absolute pixel-wise difference between corresponding pixels in two input images. The result is a new image, known as the absolute difference image. Specifically, MOG garners favor for its robustness in accommodating evolving environments, whereas the efficacy of AD hinges on pixel-wise absolute difference. In [20], it is argued that natural images possess highly structured characteristics with closely related neighboring pixels. As a result, the structural similarity index measure (SSIM) was introduced.