Abstract
The present study focuses on the challenges of human long-range recognition with deep learning, and AlexNet performance is considered, being one of the most recognized convolutional neural networks. A specially curated dataset was used to mimic the real world in a more practical and realistic scenario, which involves subjects varying in distances and angles while creating low-resolution issues with variability in lighting and distractions from the environment. The system starts by passing the input dataset through a local Laplacian filter that is utilized to enhance the quality of images before feeding it into a pre-trained AlexNet model. The results reveal that AlexNet reaches a recognition accuracy of 65% and shows that image properties such as sharpness and contrast significantly affect performance, as measured by the mean and standard deviation. The study underlines requirements of high-quality images for applications in long-range human recognition and sheds insight into the ways enhancements in images could improve performance in deep learning models. Results support further evolution of more potent human recognition systems, particularly in application domains such as surveillance and security in which the accurate identification of a target from a long distance is highly critical. Key Words: Long-range recognition, Human recognition, Deep learning, Image processing
Published Version
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