Air pollution is a universal problem confronted by many developing countries. Because there are very few air quality monitoring stations in cities, it is difficult for people to know the exact air quality level anytime and anywhere. Fortunately, large amount of surveillance cameras have been deployed and can capture image densely and conveniently. In this case, this provides the possibility to utilize surveillance cameras as sensors to obtain data and predict the air quality level. To this end, we present a novel air quality level inference approach based on outdoor images. Firstly, we explore several features extracted from images as the robust representation for air quality prediction. Then, to effectively fuse these heterogeneous and complementary features, we adopt multikernel learning to learn an adaptive classifier for air quality level inference. In addition, to facilitate the research, we construct an Outdoor Air Quality Image Set (OAQIS) dataset, which contains high quality registered and calibrated images with rich labels, that is, concentration of particles mass (PM), weather, temperature, humidity, and wind. Extensive experiments on the OAQIS dataset demonstrate the effectiveness of the proposed approach.
Modeling air quality level with a flexible categorical autoregression
