Progressive supervised pedestrian detection algorithm for green edge–cloud computing

Abstract

In this paper, we present a novel supervised pedestrian detection algorithm tailored for Green Edge–Cloud Computing (GECC), aimed at addressing the challenges associated with pre-trained models in real-world settings. These models often exhibit performance degradation due to the divergence between training datasets and complex real-world scenarios. To bridge this gap, our proposed algorithm employs a progressive occlusion-aware iterative training strategy that significantly enhances the representation of occluded pedestrians, a prevailing issue in dense urban environments. Additionally, we introduce a K-stratified hard sampling strategy, which accelerates the training process and substantially lowers the energy consumption required for model retraining. We also explore a refined non-maximal value suppression algorithm that employs neighboring centroids separation, improving the precision of pedestrian detection within crowded scenes. Through rigorous experiments on established pedestrian detection datasets, we demonstrate that our algorithm not only elevates pedestrian detection accuracy in high-density situations but also markedly reduces the model training duration.