  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.
