Personalized Ambient Pollution Estimation Based on Stationary-Camera-Taken Images Under Cross-Camera Information Sharing in Smart City

Timely and high-density air quality monitoring is essential for the development of future smart cities. The images captured from widely deployed stationary-cameras can be transferred quickly via the Internet of Things (IoT) to facilitate ambient pollution estimation anytime anywhere. Image-based air pollution estimation is normally formulated as a supervised learning problem, relying on an extended number of image samples. However, individual stationary-cameras can offer only very limited samples and scenes, while locally trained estimation models can easily overfit. A global method was proposed to address this challenge. The global model was trained via images captured from different cameras. However, such a model is less effective in extracting local features from scenes. A personalized method is therefore proposed to improve not only the generalization of the estimation model but also to preserve the local characteristics of individual cameras. Our personalized method consists of a two-stage architecture: 1) images from different cameras are used to train the global estimation model to avoid overfitting due to fixed scenes and small sample size and 2) the global model is further refined by images captured from individual cameras separately for adapting local characteristics. To evaluate our proposed personalized method, a large data set was constructed, based on stationary-camera-taken images captured in Hong Kong, consisting of different pollution measurements, including PM2.5, PM10, NO2, and O3. As compared to the local model, our proposed personalized model has reduced average MAE by 5.68% and average SMAPE by 6.82%, and improved average
$r$
by 4.69%.