## 1 Introduction  
Anomaly detection is an essential study field in visual image understanding. It is defined as an unexpected pattern recognition that is significantly different from the rest of the data. Some of the significant challenges include imbalanced data distribution, the difficulty of a generalizable feature extractor, variance in anomaly situations, and various environmental conditions in the data. There has been a massive surge in the availability of publicly available real-world datasets following recent trends. However, in most of these datasets, the class imbalance towards normal classes and lack of abnormal classes means these datasets lack diversity and are not capable of efficiently training supervised detection models [1]. In addition, labeling the data is a labor-intensive and costly endeavor. Deep learning models require large amounts of data for optimal performance, and the developed systems may have only a limited utility and sub-optimal generalization [2]. Under such cases, unsupervised anomaly detection has become the standard approach to such data distribution modeling. In such scenarios, the model is trained only with normal-designated class images to capture the data distribution. Afterward, the inference is performed with both normal and abnormal images to detect the deviation from the learned distribution of the normal data. Various approaches have been proposed [3; 4; 5] for different domains for anomaly detection [6; 7; 8]. It is generally assumed that abnormal cases differ in higher and lower dimensional space, making the latent space a vital part of anomaly detection. Recent studies propose generative adversarial networks (GANs) [9] for anomaly detection due to their efficient mapping of the data distribution of both high-dimensional and low-dimensional features [10]. Public space is defined as a place that is open and accessible to people, including roads, public squares, parks, and beaches. Creating safer public spaces requires actions such as enhancing the security level against public security threats. Unemployment and different pathologies usually cause crime and economic inequality [11]. An increase in the population also increases this threat caused by anonymity and weaker interpersonal ties [12]. It is crucial to reduce the rate of urban crime to make these public spaces safer, mainly focusing on the \"crime triangle\"--place, victim, and the perpetrator [13]. The location can also have effects on impeding the crime or facilitating it. Video surveillance cameras have become a prominent part of public spaces. Surveillance cameras are given an increased allocated budget all over the world. For example, three-quarters of the Home Office budget was allocated to surveillance-related projects from 1996 to 1998 in Great Britain [14]. In the last decade, cities in the United States have also done substantial development projects using surveillance data, 87% for areas with 250,000 or more [15]. Violence against children is defined as all forms of violence against people under 18. This act may come from parents, caregivers, peers, or strangers. It includes physical, sexual, and emotional violence as well as witnessing the violence. An estimated 1 billion children have experienced any form of violence aged 2-17 years [16]. This indicates that population-based surveillance of violence against the most vulnerable of the society, the children, and ordinary people is essential to target prevention and is endorsed in the United Nations 2030 Sustainable Development Agenda. In this paper, an unsupervised anomaly detection model for different age groups of people, namely child, adults, and elderly, is proposed to achieve this goal. The proposed model has a parallel pipeline feature extractor, a conventional cascading convolutional neural network (CNN), and a cascading dilated convolutional neural network (DCN) with a dilation rate of two.