Dynamic Adoptive Gaussian Mixture Model for Multi-Object Detection Over Natural Scenes

Recent strides in visionary technologies have profoundly impacted the domain of multi-object recognition, serving as a linchpin in transformative applications such as augmented reality integration, robotic navigation, and autonomous driving. This research introduces an innovative paradigm for multi-object detection, marked by substantial advancements in accuracy and efficiency. The proposed methodology combines Dynamic Adoptive Gaussian Mixture Model (DAGMM) with advanced feature extraction and fusion techniques. with cutting-edge feature extraction and fusion techniques. Specifically, we leverage the capabilities of HOG, Akaze and Brisk feature extraction methods to capture intricate object descriptors with a rich data profile. Subsequently, we introduce a nuanced approach involving weighted variance thresholds for feature fusion, enhancing the discriminative prowess of the extracted features. Finally, for classification, we incorporate Convolutional Neural Networks (CNN). We meticulously assessed the performance of our model with two demanding datasets: UIUC sports and Caltech-101. Our DAGMM showcased exceptional proficiency, delivering impressive segmentation accuracy rates of $86.9 \%$ and $\mathbf{8 1. 1 \%}$ over UIUC sports and Caltech-101, respectively, when evaluated against rigorously self-annotated ground truth. Furthermore, our approach achieved a substantial $87.2 \%$ object classification accuracy, validating its effectiveness.