# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 2 Related Work  
In recent years, the widespread availability of high-speed internet and advanced photography equipment has greatly simplified the process of capturing and compiling digital videos [20]. This accessibility has resulted in the generation of diverse video datasets, which have driven research into video prediction technology [2, 21]. These technologies hold potential for applications across various fields, including sports analysis, human activity monitoring, vehicle tracking, and even the documentation of routine tasks such as makeup application [22, 23]. The video prediction methods explored in this study remain at an early stage, with a particular focus on forecasting future frames containing digital posture data. Recent progress in variational autoencoder (VAE) methodologies has played a pivotal role in advancing such a domain [24]. Later, researchers have proposed a robust graph-based VAE framework capable of capturing complex spatiotemporal relationships [25]. By incorporating temporal attention mechanisms, the framework achieves enhanced reliability in long-term predictions. Also, the key challenge in video prediction lies in predicting subsequent sequences of images based on the temporal continuity of preceding frames, thus emphasising the importance of sophisticated image generation algorithms [26, 27]. Moreover, generative adversarial networks (GANs) have demonstrated their ability to achieve convergence by utilising the dynamic interaction between the generator and discriminator [28]. This approach eliminates the sole dependence on manually crafted loss functions during backpropagation. In many instances, the discriminator functions as a variant of an encoder, offering feedback that guides the generator to produce a diverse range of images. GANs have proven to be particularly effective in generating refined and distinct images, which has led to their widespread application across various network architectures. Furthermore, researchers have recently introduced a flow-based generative network that meticulously calculates the distribution space of images [29]. Furthermore, more video prediction model needs the ability to generate consistent and dynamically evolving sequences. Although image generation methods cannot be directly applied to video prediction, they provide valuable insights for developing more effective video prediction algorithms [2]. This advancement allows users greater control over image manipulation. The development and enhancement of deep learning techniques, particularly VAEs and GANs, have laid a strong foundation for their integration into video prediction tasks [30, 31]. By building on these image generation models, researchers have been able to delve into the temporal connections between successive video frames, a crucial factor for improving video prediction performance [32]. It is imperative that this approach encompasses not only the intricate details inherent within individual frames but also the interconnected spatiotemporal dynamics. Consequently, despite advancements in deep learning-based image generation, video prediction continues to be a considerably more intricate challenge [33, 34]. In order to address these challenges, significant attention has been given to the development of unsupervised deep neural networks for future video sequence prediction. Deep learning, now a cornerstone of computer vision, has fostered the evolution of unsupervised prediction models that incorporate imagery or video data. In practice, these methods often rely on predefined semantics or fully annotated datasets, which can require significant resources [35]. The framework delineated in [36] employs a \"maximum-margin\" structured output support vector machine (SVM) to facilitate early detection and local event identification. Refs. [37, 38] expanded on the \"first principles\" of structured random forest regression to predict object motion trajectories within videos. Ref.