## 2 Related Work
### Counting High-Density Crowd  
Both the ROI and LOI counting methods encounter a common issue--significant occlusion--when the crowd density is high, leading to a substantial increase in the likelihood of undercounting or overcounting. Therefore, it becomes crucial to use neural networks to collect images with a higher crowd density and extract their key features to determine the number of individuals in the images. We summarize the relevant studies from journals in Table 1 for further discussion. [PERSON] et al. [18] proposed a novel approach that entails the estimation of person flows between consecutive images and infers person densities from these flows instead of using direct regression. This approach enables us to impose significantly stronger constraints that encode the conservation of the number of individuals. [PERSON] et al. [19] proposed three parallel branch methods and dynamic weight adjustment. Initially, a ten-layer network was constructed using VGG-16, with each layer containing three parallel branches to capture information from different regions of the images and produce density maps. To ensure the quality of the final density map, the authors introduced learnable relative weights for the three density maps to merge them. The experimental phase involved testing on four datasets, namely ShanghaiTech, WorldExpo10, UCSD, and UCF_CC_50. [PERSON] et al. [20] utilized aerial drones to capture images of crowds to estimate the number of people in the current photography area. They trained the deep learning model Flounder-Net for aerial crowd density image processing and used a developed algorithm for counting. In the recognition process, the images underwent two rounds of convolution to obtain two sets of feature maps, each with six feature maps. Then, six convolutional layers were applied to the first three and the last three feature maps, resulting in feature labels from one to six. By continually conducting pairing comparisons, human features were derived from the images, leading to the final density map. [PERSON] et al. [21] addressed the common issue of having insufficient training data in density-based people counting. Many approaches, like SIFT (scale-invariant feature transform), HOG, etc., involve the manual extraction of features to increase the amount of training data. However, these methods may yield unsatisfactory results when dealing with a higher crowd density or limited training samples. To tackle this, they proposed an end-to-end deep recurrent model for dense people counting. The approach begins with the CNN automatically learning crowd features. To mitigate influences such as occlusion by trees or buildings, the authors added relevant data and marked these images as the ground truth with zero counts. Using these \"negative examples\", the crowd density counting model was trained, yielding a better counting accuracy compared to previous methods. However, the feature-point-extraction-based counting method is only suitable for dynamic situations where people are in motion. As the related studies rely on ground truth points obtained from displacements of the target objects, the method may not apply to static scenarios like waiting for an elevator or attending concerts.  
Our system utilizes MobileNet V3 for image recognition and single-stage object detection. This was developed by [PERSON], [PERSON] et al. [22]. They used a single neural network for object detection. SSD simplifies training, offers a competitive accuracy level, and achieves faster inference compared to methods with additional proposal steps. [PERSON], [PERSON] et al. [23] further addressed the need for advanced perception in agricultural robots, focusing on tomato harvesting in greenhouses at various growth stages. They introduced a unique dataset of green and reddish tomatoes, enabling real-time detection for harvesting robots using edge artificial intelligence.