Multi-Features Fusion Scene Classification Model Based on Body Skeleton and Facial Keypoints Recognition Over Large-Scale Crowd Videos

With the advancement of computer vision, methods for scene image representation have greatly improved classification performance. Nevertheless, challenges remain due to the complexity of scene images, including high variability within classes and strong similarity between classes, which continue to limit performance. This study proposes a model called Mutli-Features Fusion Scene Classification(MFF-SC) model that integrates multiple dimensions of information for scene classification. By combining human skeletal and facial keypoint information, and utilizing these features with other feature information (emotions, global distribution, population density, population size, population flow characteristics) for scene classification. The fusion of features from different sources generates a unified feature vector, which is ultimately input into an SVM classifier for classification tasks. This model integrates multi-dimensional information such as images, expressions, behaviors, and environments, effectively improving the robustness and accuracy of scene classification.