Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multi-tier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. QoS aware admission control, offloading and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multi-tier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.
Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion and bad light conditions), popular CNN detectors do not achieve very good object detection accuracy over the KITTI autonomous driving benchmark dataset. In this paper we propose three enhancements for CNN based visual object detection for ADAS. To address the large object scale variation challenge, deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better object detection at low feature map scales. In addition, soft non-maximal suppression (NMS) is applied across object proposals at different feature scales to address the object occlusion challenge. As the cars and pedestrians have distinct aspect ratio features, we measure their aspect ratio statistics and exploit them to set anchor boxes properly for better object matching and localization. The proposed CNN enhancements are evaluated with various image input sizes by experiments over KITTI dataset. Experiment results demonstrate the effectiveness of the proposed enhancements with good detection performance over KITTI test set.
Fatigued driving detection in complex environments is a challenging problem. This paper proposes a fatigued driving detection algorithm based on multi-index fusion and a state recognition network, for further analysis of driver fatigue states. This study uses a multi-task cascade convolutional neural network for face detection and facial key point detection, corrects the face according to the key points of the eye, intercepts a binoculus image to recognize the eye state, and intercepts a mouth image according to the left and right corner points to recognize the mouth state. This can improve the detection accuracy of the driver's head tilt, deflection, and so on. Next, an eye state recognition network is constructed for the binoculus image to identify the eye closure state, and a mouth state recognition network is used to identify the mouth state. Finally, a fatigue judgment model is established by combining the two characteristics of the eye state and the mouth state to further analyze the driver fatigue state. The algorithm achieved 98.42% detection accuracy on a public eye dataset and achieved 97.93% detection accuracy on an open mouth dataset. As compared with other existing algorithms, the proposed algorithm has the advantages of high accuracy and simple implementation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.