Efficient Feature Extraction Method for Detecting Vehicles from CCTV Videos Using a Machine Learning Approach

A critical task in the field of monitoring and traffic administration is the identification of vehicles in CCTV footage. The scope of the proposed work is vehicles detection from CCTV videos. This article provides a thorough analysis of three well-known feature retrieval methods for detecting vehicles in CCTV images: SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradients), and KAZE. RMSE (Root Mean Square Error), MSE(Mean Square Error), and Silhouette Score are some of the assessment measures used in this study. In terms of RMSE, MSE, and Silhouette Score, the research results show that KAZE operates better than SURF and HOG, proving that it’s better at detecting fine features and durability in a variety of lighting and settings. The novelty of proposed work is better at vehicle detecting fine features from CCTV videos. This research also emphasizes how crucial it is to use the right feature extraction methods for precise and effective vehicle identification in practical settings. Applying the KAZE produced noticeably better outcomes for each studied output parameter producing RMSE of 0.02709, MSE of 0.000115 and Silhouette Score of 0.2 respectively. The tool used for execution Jupyter Notebook and language used is python.