## 3 The Neural Network Dynamic Detection Model
### Background Subtraction Algorithm  
The background subtraction algorithm [25] is a prevalent method in computer vision utilized to segregate foreground objects (such as moving entities) from the background within video frames. This approach finds utility across numerous applications, including surveillance cameras, pedestrian detection, and vehicle counting. In the context of video surveillance or dynamic scenes, the background typically refers to the invariant or slowly changing components, whereas the foreground encompasses objects moving at a relatively rapid pace against the background. The principal objective of background subtraction is to identify and isolate these foreground objects. Table 1 lists the common background subtraction algorithms. Median filtering models the background by computing the median pixel value at each position, exhibiting robustness in scenes with high noise levels and effectively mitigating some noise. However, it tends to generate false detections in scenarios with abundant moving objects due to its insensitivity to motion. Frame differencing is highly susceptible to lighting changes and suffers from motion residue issues, making images processed with motion masks challenging for iterative computation by YOLO models. The Gaussian averaging method demands substantial computational resources and struggles to swiftly adapt to scene changes, especially in environments with frequent or rapid object movement, resulting in slow background model updates. In contrast, the BackgroundSubtractorMOG2 [26] algorithm employs a Gaussian mixture model with a more advanced background modeling strategy, including adaptive learning and updating of Gaussian model parameters for each pixel. This approach demonstrates robustness against factors like lighting changes and shadows, effectively extracting the foreground. Unlike allocating a fixed number of Gaussian distributions for each pixel, BackgroundSubtractorMOG2 [26] dynamically assigns Gaussian distributions to each pixel as needed. This means that while some pixels may have only a few Gaussian distributions, others might have more. Such dynamism enables the algorithm to better adapt to diverse scenes and background changes. With the arrival of new frames, the algorithm updates the Gaussian Mixture Model (GMM) for each pixel. Specifically, it examines the color or brightness value of each pixel in the new frame and matches it to the corresponding Gaussian distribution. The parameters and weights of the matched Gaussian distribution are then updated accordingly. Moreover, the weights of those Gaussian distributions that do not match the current observation gradually decrease until they are eliminated. Beyond basic background modeling, BackgroundSubtractorMOG2 also features shadow handling capabilities, addressing a common issue in background subtraction where the shadows of moving objects may be mistakenly identified as foreground. In summary, the BackgroundSubtractorMOG2 method exhibits stronger robustness and generalization capabilities in complex scenes compared to other mainstream algorithms, particularly in resisting interference from outdoor factors like lighting changes and shadows. Therefore, this study proposes integrating BackgroundSubtractorMOG2 as \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Characteristics** & **Advantages** \\ \hline Frame Differencing & Simple subtraction between consecutive frames & Easy to implement, low computational cost \\ Gaussian Average & Modeling each pixel with a Gaussian distribution & Handles gradual illumination \\ Gaussian Mixture Model & Modeling each pixel with a mixture of Gaussian distributions & Better at dealing with dynamic backgrounds than the simple Gaussian model, more precise \\ Median Filtering & Modeling the background using the median of a series of frames & Robust against noise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An Overview of Common Background Subtraction Algorithms.