Enhancing Vehicle Tracking and Recognition Across Multiple Cameras with Multimodal Contrastive Domain Sharing GAN and Topological Embeddings

Abstract

Listen

Translate article

Using Multimodal Contrastive Domain Sharing Generative Adversarial Networks (GAN) and topological embeddings, this study shows a new way to improve car tracking and classification across multiple camera feeds. Different camera angles and lighting conditions can make it hard for current car tracking systems to work correctly. This study tries to solve these problems. Common Objects in Context (COCO) and ImageNet are two datasets that are used in this method for training. Multimodal Contrastive Domain Sharing GAN is used for detection and tracking. It makes cross-modal learning easier by letting you see things from different camera angles. This framework lets the model learn shared representations, which makes it better at recognizing vehicles in a wider range of visual domains. The Topological Information Embedded Convolutional Neural Network (TIE-CNN) is used to re-identify the car after it has been found and tracked. This network embeds the paths of vehicles into a continuous latent space, keeping the important spatial connections needed for accurate tracking. Real-world multi-camera datasets used for experimental confirmation show that tracking accuracy and recognition performance are much better than with standard methods. The suggested framework works great in tough situations like blocked views and sudden changes in lighting, showing that it are reliable in complicated surveillance settings. This study adds to the progress in multi-camera car tracking and identification by combining geometric data analysis with deep learning methods. This method uses Multimodal Contrastive Domain Sharing GAN and topological embeddings to improve the timing and spatial coherence of tracking results. It also sets the stage for future improvements in monitoring and self-driving systems.