2021/11/20 by Ali Farouk Khalifa, Khalifa, Ali Farouk, Hesham N. Elmahdy +3
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fire Detection and Safety Systems #Machine Learning (cs.LG) #Video Surveillance and Tracking Methods #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2111.10653
19 pages 6 figures 9 Tables
arxiv created 2021/11/20 · openalex publication_date 2021/11/20 · arxiv updated 2021/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Building a small-sized fast surveillance system model to fit on limited resource devices is a challenging, yet an important task. Convolutional Neural Networks (CNNs) have replaced traditional feature extraction and machine learning models in detection and classification tasks. Various complex large CNN models are proposed that achieve significant improvement in the accuracy. Lightweight CNN models have been recently introduced for real-time tasks. This paper suggests a CNN-based lightweight model that can fit on a limited edge device such as Raspberry Pi. Our proposed model provides better performance time, smaller size and comparable accuracy with existing method. The model performance is evaluated on multiple benchmark datasets. It is also compared with existing models in terms of size, average processing time, and F-score. Other enhancements for future research are suggested.