## 1 Introduction  
With the rapid growth of urban populations, crowding in public venues, such as sports stadiums and train stations, has become increasingly problematic. Understanding the distribution of human crowds in a built environment is essential for timely and appropriate infrastructure management and service delivery. For example, uncontrolled crowding at exits causes sluggish evacuation during disasters; overly-dense crowds lead to stampedes [1]. For this reason, real-time monitoring and early warning of dense crowd flow are crucial for a variety of public safety-related applications, including public event management, safety monitoring, and disaster evacuation management. For instance, effective congestion detection and prediction can trigger timely support services such as the dispatching of human guides to direct crowd flow, the establishment of queue lines, and the planning of additional spaces and services for occupants. Closed-circuit television (CCTV) surveillance cameras are frequently put in public areas to understand crowd behaviors, detect anomalous events, and identify congestions for security and safety purposes [2]. By evaluating camera footage and using their own judgments, stakeholders such as security professionals, infrastructure operators, and crowd managers are able to monitor crowds and identify potential hazards. However, manual observation of videos is labor-intensive and error-prone, particularly when crowds are dense [3]. Therefore, it is essential to increase the role of technology in crowd management. Through interviews with crowd managers and assessment of the current state of practice, research has shown that crowd monitoring and event planning are sophisticated, but operate with minimal technology support at present [4]. In addition, crowd managers prefer to increase their use of technology and seek improved tools to assist them in their work. In recent years, leveraging computer vision algorithms and greater computational capacity, research has continued to expand towards automating the process of vision-based crowd congestion analysis. Earlier research with pixel and color-based methods were often used for people recognition, tracking, and crowd counting until the more recent rise of convolutional neural network (CNN) based approaches, which have been shown to be more robust under scenarios of varying lighting conditions, image resolution and sizes [5]. CNNs are particularly effective at image recognition and processing applications due to their convolutional kernels, which extract visual features while preserving the spatial relationships between pixels. Numerous CNN-based approaches, including YOLO [6], Faster R-CNN [7], and Mask R-CNN [8], have demonstrated remarkable detection abilities for a variety of objects, including humans. However, two research gaps remain between human detection models and their direct applicability to congestion monitoring. Firstly, dynamic crowd mobility information should be learned by fusing both spatial and temporal features. Existing studies rarely consider the spatial connectivity of the surrounding space, such as the location of the egresses, which is essential for congestion monitoring and management. Secondly, while there is much research applying computer vision to the detection, tracking and prediction of individual pedestrian movements, macroscopic crowd congestion statistics such as crowd count, density, and flow estimation have received less attention. The main objective of this paper is to illustrate an application of computer vision and machine learning methods to enhance crowd safety in a public space. Using deep learning and computer vision techniques, this paper proposes to fuse features from CCTV footage and spatial information of the public space to establish a framework for monitoring crowd congestion in public urban spaces.