VisualTrackMapper: An Efficient Framework for Advanced Object Recognition Using Deep Learning Procedure

There has been a lot of focus on object detection in recent years from researchers because of its interconnectedness with picture understanding and video analysis. The foundation of conventional object identification systems is features that are hand -crafted and architectures that are shallow and trainable. They reach a performance ceiling when presented with complex ensembles that combine low-level image data with high-level information from object detectors and scene classifiers. New, more effective tools are being offered to solve the issues with old architectures as a result of the fast growth of deep learning. These tools can learn semantic, high-level, deeper characteristics. When it comes to things like training strategy, optimization function, network design, etc., these models act differently. To efficiently detect objects from the input image, this paper introduces a novel deep learning approach called Neural Feature Mapping Strategy (NFMS). To evaluate its efficiency, it is cross-validated with the conventional learning model called Convolutional Neural Network (CNN). According to different detection frameworks, most object recognition algorithms that are now available that are based on deep learning use multi-stage detection. A common data source, Kaggle, provided the dataset used in this approach, and the OpenSource tool, Python, was responsible for the implementation. Finally, the performance and accuracy of the proposed NFMS model compared to the existing CNN model on these criteria have been empirically presented in the results section. So, compared to the current model, the proposed model has superior accuracy.