## 2 Related Work  
Starting with AlexNet's [26] superior performance in The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) against conventional methods, deep learning models have also created a new spark of interest in anomaly detection. With broad real-world applicable areas such as video surveillance [5], and biomedical engineering [4], a large number of papers have been published using anomaly detection [27]. The proposed model in this paper also follows recent reconstruction-based trends.  
An influential model proposed by [PERSON] et al. [4] uses adversarial training. In the paper, it is hypothesized that the latent vector of the GAN represents the data distribution. However, the authors initially train a generator and a discriminator with only normal images to achieve this. Afterward, using the frozen weights of the pre-trained generator and discriminator, they remap to the latent vector by optimizing the model based on its latent vector. The model shows an anomaly by showing a high anomaly score during the testing, a significantly better score than previous works. The main drawback of this proposal is the computational complexity due to the two-stage approach, and latent vector remapping is computationally costly. Following this work, [PERSON] et al. [28] use BiGAN [29], applying joint training to map the data distribution from the image and the latent space. [PERSON] et al. [10] propose an autoencoder (GANomaly) with an additional encoder added to the decoder's end to train this autoencoder and the discriminator jointly. Afterward, [PERSON] et al. [30] propose an additional anomaly detection model with skip-connections trained jointly with the discriminator.  
Human age in the literature is generally classified into four major categories: child (0-12 years), adolescent (13-18), adult (19-59), and senior adult (60 and above) [31]. Most of the age group classification approaches are either gait-based [31] or focus facial features [32]. These approaches range from using support vector machines [33] to complex CNN architectures [34]. However, the major issue with these approaches is that the choice of the dataset is labeled. These approaches have minimal use for real-time surveillance usage. External conditions such as lighting, image quality, positioning of the pedestrians, occlusion through other people, and other objects often cause less-than-ideal scenarios. The proposed model is trained to distinguish the abnormal and normal images and jointly minimize the distance between their latent vector representations.  
As reconstruction-based approaches [4; 10; 28; 30] show promising results in anomaly detection; a solution to this problem is the proposed unsupervised anomaly detection approach, where the input data does not have a label. Surveillance camera footage captured from regions in the Republic of Korea is used to train the proposed model. The pedestrians observed in the surveillance cameras are selectively cropped and are manually labeled with age classes, namely, the child, adult, and elderly. A total of 80% of the normal classes in a cluster to learn the data distribution of these classes is used for training. For comparison, the remaining 20% of the normal class images and an equal amount of corresponding anomalous class images are input through the model. An anomaly score is obtained for each image. The abnormal image's deviation from the normal distribution is shown in its anomaly score compared with normal images, and this is used to detect outlier cases.