F3DsCNN: A Fast Two-Branch 3D Separable CNN for Moving Object Detection

Deep learning methods have been actively applied in moving object detection and achieved great performance. However, many existing models render superior detection accuracy at the cost of high computational complexity and slow inference speed, which hindered the application on mobile and embedded devices with limited computing resources. In this paper, we propose a two-branch 3D separable convolutional neural network named “F3DsCNN” for moving object detection. The network extracts both high-level global features and low-level detailed features. It achieves a fast inference speed of 120 frames per second, suitable for tasks that need to be carried out in a timely manner on a computationally limited platform with high accuracy.