Comparative analysis of feature descriptors and classifiers for real-time object detection

Abstract

Detecting objects within complex environments, such as urban settings, holds significant importance across various applications, including driver assistance systems, traffic monitoring, and obstacle detection systems. Particularly crucial for these applications is the accurate differentiation between cars and roads. This study introduces a novel approach that leverages traditional feature descriptors and classifiers for real-time object detection. It conducts an exhaustive comparative analysis of feature descriptors and classifiers to identify the most effective model for real-time object detection. Handcrafted features of images are extracted using algorithms such as scale invariant feature transform (SIFT), oriented fast and brief (ORB), fast retina key-point (FREAK), and local binary pattern (LBP). Seven classifiers are employed, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), logistic regression (LR), Naive Bayes, and extreme gradient boosting (XGBoost). The performance of the 28 generated combinations of feature descriptors and classifiers is evaluated based on the parameters of accuracy, precision, F1 score, and recall. The model utilizing LBP and XGBoost achieves the highest accuracy, reaching 83.59%. The system architecture comprises a camera, a high-speed computing unit, a display, and an audio subsystem, with the algorithm implemented on a Raspberry Pi 4B (8 GB).