Domain adaptation from daytime to nighttime: A situation-sensitive vehicle detection and traffic flow parameter estimation framework

Abstract

Vehicle detection in traffic surveillance images is an important approach to obtain vehicle data and rich traffic flow parameters. Recently, deep learning based methods have been widely used in vehicle detection with high accuracy and efficiency. However, deep learning based methods require a large number of manually labeled ground truths (bounding box of each vehicle in each image) to train the Convolutional Neural Networks (CNN). In the modern urban surveillance cameras, there are already many manually labeled ground truths in daytime images for training CNN, while there are little or much less manually labeled ground truths in nighttime images. In this paper, we focus on the research to make maximum usage of labeled daytime images (Source Domain) to help the vehicle detection in unlabeled nighttime images (Target Domain). For this purpose, we propose a new situation-sensitive method based on Faster R-CNN with Domain Adaptation (DA) to improve the vehicle detection at nighttime. Furthermore, a situation-sensitive traffic flow parameter estimation method is developed based on the traffic flow theory. We collected a new dataset of 2,200 traffic images (1,200 for daytime and 1,000 for nighttime) of 57,059 vehicles to evaluate the proposed method for the vehicle detection. Another new dataset with three 1,800-frame daytime videos and one 1,800-frame nighttime video of about 260 K vehicles was collected to evaluate and show the estimated traffic flow parameters in different situations. The experimental results show the accuracy and effectiveness of the proposed method.