Unifying terrain awareness for the visually impaired through real-time semantic segmentation.

Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Deep passenger state monitoring using viewpoint warping

The advent of autonomous and semi-autonomous vehicles has meant passengers now play a more significant role in the safety and comfort of vehicle journeys. In this paper, we propose a deep learning method to monitor and classify passenger state with camera data. The training of a convolutional neural network is supplemented by data captured from vehicle occupants in different seats and from different viewpoints. Existing driver data or data from one vehicle is augmented by viewpoint warping using planar homography, which does not require knowledge of the source camera parameters, and overcomes the need to re-train the model with large amounts of additional data. To analyse the performance of our approach, data is collected on occupants in two different vehicles, from different viewpoints inside the vehicle. We show that the inclusion of the additional training data and augmentation by homography increases the average passenger state classification rate by 11.1%. We conclude by proposing how occupant state may be used holistically for activity recognition and intention prediction for intelligent vehicle features

Sleep-wake profiles in patients with primary biliary cirrhosis

BACKGROUND: Impaired sleep quality and daytime sleepiness have been described in patients with primary biliary cirrhosis (PBC). However, no information is available on their sleep timing/diurnal preference. AIMS: To evaluate such variables and determine their relationship with sleep quality, fatigue, pruritus and quality of life. METHODS: Seventy-four patients with PBC (58 \ub1 12 years), 79 healthy volunteers (56 \ub1 8 years) and 60 patients with cirrhosis (58 \ub1 12 years) underwent formal assessment of sleep quality/timing, diurnal preference and daytime sleepiness. Patients with PBC also underwent assessment of fatigue, quality of life and the daytime course of sleepiness/pruritus. RESULTS: Sleep timing was significantly delayed in both patients with PBC and with cirrhosis, compared to healthy volunteers (sleep onset time: 23:18 \ub1 01:00 vs. 23:30 \ub1 01:00 vs. 22:54 \ub1 00:54 hours, respectively; P < 0.05). In patients with PBC, delayed sleep timing was associated with impaired sleep quality (P < 0.05). Sleepiness showed a physiological daily rhythm, with early afternoon/evening peaks. Pruritus was absent in the morning and increased over the afternoon/evening hours. Both the daytime course of pruritus and sleepiness changed in relation to diurnal preference. Patients with PBC and significant pruritus (upper quartile) had prolonged sleep latency (39 \ub1 37 vs. 21 \ub1 23 min, P = 0.05) and earlier wake-up times (5.9 \ub1 0.8 vs. 6.7 \ub1 0.9 min, P < 0.05). Significant correlations were observed between sleep timing and quality of life. CONCLUSIONS: Patients with PBC exhibited a delay in sleep timing that was associated with impaired sleep quality/quality of life. In addition, an interplay was observed between diurnal preference and the daytime course of pruritus/sleepiness

Driver Drowsiness Warning System Using Visual Information for Both Diurnal and Nocturnal Illumination Conditions

Every year, traffic accidents due to human errors cause increasing amounts of deaths and injuries globally. To help reduce the amount of fatalities, in the paper presented here, a new module for Advanced Driver Assistance System (ADAS) which deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, track, and analyze both the drivers face and eyes to compute a drowsiness index, where this real-time system works under varying light conditions (diurnal and nocturnal driving). Examples of different images of drivers taken in a real vehicle are shown to validate the algorithms used