convolutional neural networks (CNNs)

Object recognition systems often struggle to maintain accuracy in dynamic environments due to challenges such as lighting variations, occlusions, and limited training data. Traditional convolutional neural networks (CNNs) require extensive labeled datasets and lack adaptability when exposed to new conditions. This study aims to develop an adaptive object recognition framework that enhances model generalization and rapid adaptation in changing environments.

Pedestrian segmentation is a critical task in computer vision, but it can be challenging for segmentation models to accurately classify pedestrians in images with challenging backgrounds and luminosity changes, as well as occlusions.  This challenge is further compounded for compressed models that were designed to deal with the high computational demands of deep neural networks.  To address these challenges, we propose a novel approach that integrates a region proposal-based framework into the segmentation process.  To evaluate the performance of the proposed framework,