Finding autofocus region in low contrast surveillance images using CNN-based saliency algorithm

Abstract

How to automatically locate the focus region in low contrast image is a key issue for camera-equipped surveillance devices. Due to low signal to noise ratio, the performance of autofocus will seriously decline in low contrast image, making it quite difficult to recognize the focus region. To tackle this problem, we perform autofocus by conducting a salient object detection approach. A covariance based deep learning framework is proposed to evaluate the saliency of low contrast surveillance image. Based on the mechanism of human visual system, the autofocus region can be identified by the visual salient object. In this paper, low-level features of the low contrast images are first studied and extracted. Then the mutual covariances of the segmented blocks are trained via a 7-layers convolutional neural network (CNN). Next, the initial saliency map of the testing image can be obtained by estimating the saliency score of each block via the pre-trained CNN model. Finally, the resulting saliency map is refined by introducing the local-global difference and internal similarity approaches. Experimental results demonstrate that the proposed method outperforms existing ten state-of-the-art saliency models on three public datasets and a nighttime image dataset.