# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 2 Relevant Work  
In most countries, using closed-circuit television (CCTV) monitoring systems is the major method of ensuring safety in restricted areas with significant traffic [5]. Video surveillance is an inescapable part of life in today's world. It increases public safety by allowing the area to be monitored in the event of a sudden congestion. Basic systems consist of a set of cameras that send a signal to a central location where the video streams are shown on screens and recorded, and they just provide recording and display capabilities. They might either passively collect the footage or be monitored actively. CCTV is useful in a proactive approach to safety, where conditions are established to prevent accidents rather than waiting for them to happen. One technique to lessen the chances of an accident is to apply behavior-based safety (BBS) approaches [6]. Analyzing video data to uncover and highlight crucial elements for assisted scene interpretation and analysis would be a simple upgrade to the system. They recognize, categorize, track, locate, and evaluate the activity of objects of interest in the environment using computer vision algorithms or video motion detectors. To be able to obtain insight from the movements of items in the camera frame, it is necessary to first detect such objects. There has been a lot of progress in computer vision systems that can be used to detect and extract the geolocation of points recently. These unstructured data points can be readily transferred to a data structure that machine learning algorithms can exploit to gain more insights with simple post-processing. [PERSON] et al. devised a system for detecting moving objects in surveillance footage and determining whether or not they are human [7]. To account for faster object recognition, they used a well-known Background Subtraction Algorithm known as Mixture of Gaussians. To work with, the Support Vector Machine (SVM) algorithm is provided with a set of basic and efficient properties. Several SVM kernels, as well as the K Nearest Neighbor Classifier's numerous distance measurements, are used to evaluate the system's performance. The study yielded an F measure of 86.925% on average. The background subtraction algorithm is based on the assumption that background is more often visible than the foreground, and background variance is low. This is modeled using the equation [7]  
\[P(X_{t})=\sum_{i=1}^{\mathrm{k}}\omega_{i,t}\eta(X_{t},\mu_{i,t},\Sigma_{i,t}) \tag{1}\]  
where  
* The number of Gaussian clusters used to describe pixel history is denoted by k;
* \(\omega_{i,t}\) is the weight factor associated with cluster \(i\) and time \(t\);
* \(\mu_{i,t}\) and \(\Sigma_{i,t}\) are the mean and covariance matrix of \(i\)-th Gaussian cluster. With the emergence of convolutional neural networks (CNN), the capacity to bridge the gap between human and computer talents has increased dramatically. To obtain fantastic outcomes, researchers focus on a number of components in the sector. A Convolutional Neural Network (CNN) is a Deep Learning system that can take an input image, assign value to distinct objects or areas in the image using learnable weights and biases, and distinguish between them [8]. The amount of pre-processing required by a CNN is much less than that required by other classification algorithms. While early techniques include hand-engineering of filters, with adequate training, CNNs may quickly learn how to configure filters. A CNN successfully captures the Spatial and Temporal correlations in a picture by applying appropriate filters. The architecture provides better fitting to the picture dataset because of the reduced number of parameters involved and the reusability of weights. To put it another way, the network might be honed to better understand the image's complexity. A CNN, on the other hand, can only classify a single image and fails when there are many objects in the image.