Real-time Human Detection Model for Edge Devices
This work addresses the need for efficient surveillance systems on resource-limited devices, but it appears incremental as it builds on existing lightweight CNN approaches.
The paper tackles the problem of building a small, fast human detection model for edge devices like Raspberry Pi, achieving better performance time, smaller size, and comparable accuracy compared to existing methods.
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.