Detecting Background Dynamic Scenes using Naive Bayes Classifier Analysis Compared to CNN Analysis

Surveillance, object tracking, and autonomous vehicles rely on background dynamic scene detection and analysis. This paper compares the Naive Bayes classifier and CNNs for background emotional scene recognition. Background passionate scenes provide obstacles with complicated and unpredictable motion patterns, and the study begins. A professionally managed dataset includes moving items, changing illumination, and environmental variations. Pre-processing and annotation help with training and evaluation. Background dynamic scene detection begins with Naive Bayes. Naive Bayes, a probabilistic approach that presupposes feature independence, is suitable for classification. Different feature extraction methods and Naive Bayes algorithms are used to evaluate background dynamics capture. Next, CNN algorithms consider the dataset. CNNs are great at detecting spatial connections in image and video data. To maximize background dynamic scene identification, CNN architectures are created and trained using different network depths, kernel sizes, and pooling algorithms. Accuracy, precision, recall, and F1-score are used to evaluate Naive Bayes and CNN algorithms. The results are extensively evaluated to assess each method's background dynamic scene-handling capabilities.