Improving detection of moving pedestrian in surveillance systems / Ali Farouk Ali Mohamed Khalifa ; Supervised Hesham Nabih Elmahdy , Eman Mostafa Badr

Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Technology

Building reliable surveillance systems is critical for security and safety. A core component of any surveillance system is the human detection model. With the recent advances in the hardware and embedded devices, it becomes possible to make a real-time human detection system with low cost. Different systems and techniques that have been deployed on embedded devices such as Raspberry Pi are surveyed. The characteristics of datasets, feature extraction techniques, and machine learning models are covered. A unified dataset is utilized to compare different systems with respect to accuracy and performance time. 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 work 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 other state-of-the-art models in terms of size, average processing time, and F-score. In addition, some methods are suggested to be adapted to further enhance the model in terms of accuracy, size and performance time

Issued also as CD