Adaptive Boosted Random Forest for Object Detection

Object detection is a crucial task in computer vision with applications in surveillance, autonomous driving, and security. Traditional Random Forest (RF) models, while effective, suffer from limitations such as overfitting and suboptimal feature selection. To address these issues, we propose an Adaptive Boosted Random Forest (ABRF) framework that integrates the robustness of Random Forest with the adaptiveness of the AdaBoost algorithm. The proposed model assigns weights to misclassified instances, allowing the RF classifier to iteratively improve its predictive performance.

Keywords:
Object Detection, Adaptive Boosting, Random Forest, Machine Learning, Feature Selection, Classification, Ensemble Learning

Object Detection Adaptive Boosting Random Forest Machine Learning Feature Selection Classification Ensemble Learning