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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver 3D absolute gaze point obtained through the combined use of a front-view...
Performance measures and adaptive control methodologies for traffic signal systems currently require intersections to be instrumented with vehicle detectors and communication equipment, which can require substantial engineering resources to deploy and maintain. Recent studies have explored the use of Connected Vehicle (CV) data for signal performance measures at various levels of market penetration,...
Visualizing road traffic datasets involves representing junctions, their links, and the attributes of those links. Current traffic visualization techniques are not sufficient for professional traffic engineers, as they are limited in the number of attributes that can be represented. This paper proposes a new approach to visualize multiple attributes on graph edges without compromising their visibility...
Reduced visibility on roadways caused by localized fog can impact the traffic flow in many ways: traffic speed, travel time delay, reduced capacity and accident risks. This paper presents a novel approach to estimate visibility conditions using an onboard camera and a digital map. Based on a traffic sign detector's characteristics in the fog, and registering detection by vision and information encoded...
Obstacle detection for advanced driver assistance systems has focused on building detectors for only a few number of object categories so far, such as pedestrians and cars. However, vulnerable obstacles of other categories are often dismissed, such as wheel-chairs and baby strollers. In our work, we try to tackle this limitation by presenting an approach which is able to predict the vulnerability...
We here study the problem of visual attention computation in video of driving environment via the learning from eye movements. We collect a large-scale database of eye movements from 28 subjects on 30 videos of road scenes, which simulate the driving environment. The analysis on this eye movement database reveals that visual attention in driving environment is directed by high-level cognitive factors...
A control method of variable lane based on video detection is proposed in this paper to handle the adaptive switching of the lane's oriented attribute. Based on the image calibration, visual loop detectors are built up in the surveillance video of the intersection. Then, the characteristics of the traffic flow, such as flow rate, velocity, queue length, can be obtained by the video detection based...
This paper proposes a generic approach combining a bottom-up (low-level) visual detector with a top-down (high-level) fuzzy first-order logic (FOL) reasoning framework in order to detect pedestrians from a moving vehicle. Detections from the low-level visual corner based detector are fed into the logical reasoning framework as logical facts. A set of FOL clauses utilising fuzzy predicates with piecewise...
Tracking-by-detection is an attractive paradigm for intelligent visual surveillance applications where clutter, lighting variations, target overlap and occlusions hamper conventional background modeling. However, state-of-the-art vehicle and pedestrian detectors based on discriminative classification are too computationally expensive for real-time implementation on embedded smart cameras. This paper...
Vision based lane detection is an essential task in both autonomous lane vehicles research and active safety system development. Hitherto, lane detection is, however, still a challenging issue due to the complexity of the real road scenes. In this paper, we consider lane detection as a visual attention problem. With a Bayesian attention framework, we address the issue from three perspectives: frst,...
Protection of large and complex urban areas from radiological threats may be improved by employing a network of distributed radiation detectors. Among the many considerations involved in designing such a system are detector type, concept of operations, methods to collect and extract meaningful information from multiple data sources, and cost. We have developed a realistic simulation environment as...
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