Haze weather recognition based on multiple features and Random Forest A single image based haze weather recognition is the fundamental operation of the applications of outdoor computer vision. Currently, the recognition results are remains undesirable and most existing methods are only for the fixed scene. In this paper, we propose multiple features and Random Forest based haze weather classification method for any scenario to improve the detection accuracy. First, through systematically investigation, multiple features are extracted and properly processed. Then, these features are combined into high dimension vectors and the Random Forest is adopted to lean an adaptive classifier for haze recognition. In the experiment, an outdoor image set which contains around 4000 images is collected. Form the experimental results is can be seen that the proposed method achieves 97.4% recognition accuracy of the haze weather on the collected dataset.

Weather Classification on Roads for Drivers Assistance using Deep Transferred Features Extreme weather conditions such as heavy rain, fog and scorching heat of sun increase the risk factor of road accidents and traffic congestion. This impels valuable lives and property into tremendous dangers and causes dilatory. Therefore, automatic weather detection on roads for the assistance of drivers becomes very crucial. Weather detection and forecasting is usually figured out by using temperature, humidity and wind sensors. In this paper, we introduce a methodology which exploits visual data to detect the weather condition. We propose a weather detection system based on stateoftheart deep learning techniques by transferring the learned weights of pretrained inception v3 model to our problem. Proposed system can be utilized to generate alerts for upcoming vehicleu0027s drivers to change their driving behavior according to the weather condition. The system can also be helpful for Intelligent Transportation System (ITS) authorities so that they can provide road safety to the citizens with efficient way and improve the current ITS. The system is trained using selfgenerated dataset. Proposed system achieved an accuracy of 98% on unseen dataset, which is very high as compare to prior models.