## 1 Introduction  
Object detection is a computer technology related to computer vision and image processing that aims to determine and detect many target objects from still images or video data [1]. It widely comprises different important techniques, namely image processing,pattern recognition, artificial intelligence (AI), and machine learning (ML). It finds applicability in different domains such as road traffic accident prevention, theft detection, traffic management, etc. [2; 3]. Intelligent video surveillance is a vintage subject in the domain of image processing and computer vision that has recently become well known. It has numerous significant benefits, such as accurate data processing, low human resource cost, and effective information gathering organization [4]. Crowd density estimation is regarded as a significant application in visual surveillance, and it plays an important role in crowd management and monitoring. Specifically for service providers in public areas, a crowd density estimation system could show how many consumers are currently waiting and therefore provide an appropriate reference to send the bounded sources reasonably and effectively [5].  
Crowd density estimation refers to the assessment of crowd dispersal and the particular number of people [6]. Crowd analysis has attracted substantial interest among researchers in recent years because of various reasons. The massive increase in the global population and in urbanization has resulted in an increase in such events as public demonstrations, sporting events, political rallies, and so on. Similar to other computer vision issues, crowd analysis faces numerous difficulties, such as inter-scene variations in appearance, occlusions, uneven distribution of people, high clutter, intra-scene and scale issues, non-uniform illumination, and an unclear viewpoint; these problems are immensely difficult to solve [7; 8]. The perplexity of the issue along with the extensive array of applications for crowd analysis has led to an increased focus among research scholars in recent years.  
Convolutional neural network (CNN) models [9; 10] have reached successful outcomes in image processing and in the prediction of crowd density. Recent crowd density estimation methodologies are primarily dependent on regression or identification. Detection approaches can be implemented in cases that have a minimum number of persons and no occlusion-like detectors depending on closer frames [11; 12]. Other methodologies that depend on regression are of two categories. The first includes those that identify handmade features in the image, namely texture feature and edge feature; next, regression function is selected for estimating aggregate person numbers [13]. Another one relies on deep neural networks and density map regression, and this technique is considered the best for estimating crowd density.  
This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model on video surveillance systems. The proposed MDTL-ICDDC technique leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) technique was employed for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for effectual parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC technique is carried out using benchmark dataset.  
The rest of the paper is organized as follows. Section 2 offers a brief survey of recently developed crowd density estimation and classification models. Next, Section 3 provides the proposed MDTL-ICDDC technique for crowd classification on surveillance videos. Then, Section 4 validates the performance of the proposed model, and finally, Section 5 concludes with the major key findings of the work.