Visibility estimation via deep label distribution learning in cloud environment

The visibility estimation of the environment has great research and application value in the fields of production. To estimate the visibility, we can utilize the camera to obtain some images as evidence. However, the camera only solves the image acquisition problem, and the analysis of image visibility requires strong computational power. To realize effective and efficient visibility estimation, we employ the cloud computing technique to realize high-through image analysis. Our method combines cloud computing and image-based visibility estimation into a powerful and efficient monitoring framework. To train an accurate model for visibility estimation, it is important to obtain the precise ground truth for every image. However, the ground-truth visibility is difficult to be labeled due to its high ambiguity. To solve this problem, we associate a label distribution to each image. The label distribution contains all the possible visibilities with their probabilities. To learn from such annotation, we employ a CNN-RNN model for visibility-aware feature extraction and a conditional probability neural network for distribution prediction. The estimation result can be improved by fusing the predicting results of multiple images from different views. Our experiment shows that labeling the image with visibility distribution can boost the learning performance, and our method can obtain the visibility from the image efficiently.