A Super-Fast Deep Network for Moving Object Detection

Deep learning methods have been actively applied to intelligent video surveillance for moving object detection in recent years and demonstrated impressive results. However, these models render superior accuracy at the cost of high computational complexity. In this work, we devised a new deep network structure that significantly improves inference speed, yet requires 10 times smaller model size and achieves 10 times reduction in floatingpoint operations as compared to existing deep learning models with tolerable accuracy loss.