# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 2 Relevant Work
The length of the output layer can be changed, which is why a normal convolutional network followed by a fully connected layer will not fix the problem. That gets us to the Region-based Convolutional Neural Network (R-CNN), which is a better version of the CNN. The concept of region suggestions is central to the R-CNN series [9]. Region suggestions are used to locate items inside a photograph. To overcome the issue of selecting a high number of areas, [PERSON] et al. proposed region recommendations, a method that uses selective search to extract only 2000 regions from a picture. As a result, instead of attempting to categorize a vast number of places, it is now possible to focus on just 2000. With further enhancements to this technology, faster R-CNN can be implemented. It works similarly to R-CNN, but instead of using a selective search strategy on the feature map to produce region proposals, it uses a separate network to anticipate area ideas. After that, a Region of Interest (Rol) pooling layer is used to categorize the image within the suggested region and forecast the bounding box offset values, and the projected region proposals are reshaped. The overall speed is significantly higher than that of R-CNN. The quicker R-CNN, on the other hand, clocked in at only 18 frames per second and had a mean average precision of 62.1. Real-time is not defined as 18 frames per second [10]. The You Only Look Once, or YOLO, algorithm is used to help with this. YOLO is based on a very different concept. This network does not take a comprehensive look at the situation. Rather, it looks at areas of the image that have a high chance of containing the item. YOLO is a method for detecting objects that differs dramatically from the previously stated region-based strategies. In YOLO [11], a single neural network predicts the bounding boxes and class probabilities for these boxes. The image is divided into grids, with bounding boxes within each grid. This is how YOLO works. For each bounding box, the network generates a class probability and bounding box offset values. To find the item in the picture, bounding boxes with a class probability greater than a threshold value are picked and used. YOLO is thousands of times faster than traditional object identification algorithms. [PERSON] et al. employed the YOLO-PC method, a unique extension of the YOLO algorithm, to count persons across frames and reported a frame rate of 40 frames per second, which is fast enough to be considered real-time given that video cameras typically shoot at 20 frames per second [12]. In subsequent iterations of the YOLO algorithm, it has managed to improve the computation speed significantly. The YOLOv4 algorithm developed by [PERSON] et al. managed to achieve 65 frames per seconds with 43.5% average precision [13]. In the literature, the use of clustering to cluster moving points is well documented. [PERSON] proposed a new method for categorizing moving object trajectory data that extends the k-means algorithm. In that study, the number of clusters for the k-means approach was decided by the number of dissimilar patterns found during the selection phase. The authors further urged that the centroids be initialized based on trajectory dissimilarity, citing the impact of initial centroid choice on clustering quality and the potential for dead centroids [14]. [PERSON] also looked at using a server to perform continuous k-means computations while watching a group of moving objects. GPS device positions were transferred to a central server. The server maintained a record of these places and regularly updated the results of registered spatial queries. The authors focused on continuous k-means monitoring of moving objects after that [15]. HDBSCAN is a powerful data exploration tool. When the data in datasets have a wide range of distributions, HDBSCAN excels. It does a good job of dealing with the edge cases of other algorithms. Clusters of various densities, cluster sizes, and cluster shapes are among the examples.