# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 3 Proposed Method
### Enhanced Feature Extraction and Sequence Decoupling  
In order to predict the future position of handwritten digits in video sequences, a decoupling-based approach is employed to separate the target object from the background. This method optimises computational efficiency by minimising the number of parameters required for training and accelerating the network model's convergence. The decoupling module uses an autoencoder-style encoder to isolate the target's dynamic features while preserving background information. As illustrated in Figure 1, the framework consists of several key components. This diagram illustrates the process by which the input video is first processed via the ChebyPooling layer to extract robust spatial features. It is then separated into foreground and background components using a modified DCGAN. Next, temporal modelling is performed using ConvCARU. Finally, the video is fed into classification layers that combine FC and Conv2D operations. Figure 1: A proposed neural architecture for spatiotemporal feature learning using enhanced discriminators. Video feature extractionThe initial processing step involves using ConvCARU to refine the input features by applying a series of convolutional operations, followed by a ChebyPooling layer [59], batch normalisation, and activation functions. This structure ensures the efficient encoding of temporal dependencies within the video frames. Spatial feature predictionA sequence of convolutional layers extracts spatial attributes using multi-channel feature mapping and activation functions, such as Maxout, as well as normalisation techniques. These spatial descriptors provide valuable information about the object's positional variations. Feature fusionThe extracted video and spatial features are integrated to improve contextual perception. This mechanism uses attention layers to prioritise the most relevant information from different sources. Feature aggregationA DCGAN-based aggregation unit is used to process feature fusion, with the aim of further refining structured patterns before classification. Final classificationThe final prediction is generated by processing the refined feature representations through FC layers with dropout regularisation and activation functions (e.g., Sigmoid or Softmax). To improve the efficiency and accuracy with which the target extraction module identifies and tracks target positions across frames, a discriminator is incorporated to form a complete DCGAN that optimises the feature extraction process. In our implementation, we focus on a unified scheme rather than employing multiple alternative approaches. Specifically, the architecture employs the following: ConvCARU for temporal modellingThis component is responsible for capturing and propagating spatio-temporal dependencies with convolutional operations embedded in its gating mechanisms. By avoiding the use of traditional fully connected (FC) layers within the recurrent unit, it preserves crucial spatial information and improves parameter efficiency. Modified DCGAN for background and foreground separationRather than integrating several potential pooling or segmentation strategies, we adopted a modified version of DCGAN that replaces conventional pooling with strided convolutions. This approach stabilises training and effectively decouples the dynamic (foreground) from the static (background) features. ChebyPooling for feature abstractionWe incorporate a ChebyPooling layer, which uses the Chebyshev polynomial formulation to enhance the robustness of the pooling process, ensuring that spatial details are accurately retained. Moreover, this structured approach enhances the accuracy and computational efficiency with which an object's motion trajectory can be predicted. The framework's ability to dynamically track objects across video sequences is strengthened by the integration of attention mechanisms and advanced feature aggregation techniques.