Fast Vehicle Detection and Tracking On Fisheye Traffic Monitoring Video Using Cnn and Bounding Box Propagation

The accuracy of deep neural networks is easily degraded by image corruption. Therefore, there is a need to develop adaptation techniques to ensure durable models and predictions against changes in data distribution. We focus on the task to fit a trained model with a different distribution from training data under the condition that the training data are not available for test time. in this paper, we propose a novel adaptation method in test time for online learning named multi-step layer adaptation (MuSLA). The proposed method achieves high adaptive accuracy by sequentially applying loss functions to specific layers only, especially considering the roles and interactions of the layers and employing domain adaptation and semi-supervised learning techniques. The proposed method can be widely applied to already existing trained models without additional networks. We show that our approach outperforms conventional methods in image corruption benchmark data experiments.