In advanced industrial applications, computational visual attention models (CVAMs) could predict visual attention very similarly to actual human attention allocation. This has been used as a very important component of technology in advanced driver assistance systems (ADAS). Given that the biological inspiration of the driving-related CVAMs could be obtained from skilled drivers in complex driving conditions, in which the driver's attention is constantly directed at various salient and informative visual stimuli by alternating the eye fixations via saccades to drive safely, this paper proposes a saccade recommendation strategy to enhance the driving safety under urban road environment, particularly when the driver's vision is often impaired by the visual crowding. The altered and directed saccades are collected and optimized by extracting four innate features from human dynamic vision. A neural network isdesigned to classify preferable saccades to reduce perceptual blindness due to visual crowding under urban scenes. A state-of-the-art CVAM is firstly adopted to localize the predicted eye fixation locations (EFLs) in driving video clips. Besides, human subjects' gaze at the recommended EFLs is measured via an eye-tracker. The time delays between the predicted EFLs and drivers' EFLs are analyzed under different driving conditions, followed by the time delays between the predicted EFLs and the driver's hand control. The visually safe margin is then measured by mediating the driving speed and the total delay. Experimental results demonstrate that the recommended saccades can effectively reduce the amount of perceptual blindness, which is known to be of help to further improve road driving safety [1].

Single image deraining task aims at removing rain streaks from a degraded input and reconstructing the high-quality image. In recent years, image processing tasks mostly apply the U-shaped architecture to capture rich contextual information. However, it is difficult to achieve long-range pixel dependencies due to the local receptive field of the convolution operation. In this paper, we propose a deep feature interactive aggregation network for single image deraining to enhance the long-range dependencies among features and realize the interaction of information. To fully utilize the high-level semantic features, we design a long-range dependency feature aggregation module to greatly improve the representational ability of the original U-shaped architecture. It aggregates multi-scale features and calculates the interactive attention of non-overlapping patches among feature maps. In addition, we adopt group normalization to retain the independence of each given image. It interacts with the information among features in an individual image and normalizes the channels of each group to weaken the correlation between batch data processing. Experimental results on widely acknowledged datasets also demonstrate the superiority of our proposed network over previous state-of-the-art methods.INDEX TERMS Deep network, image deraining, transformer.

No abstract

No abstract

No abstract