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Accurate detection and localization of vehicles in aerial images has a wide range of applications including urban planning, military reconnaissance, visual surveillance, and realtime traffic management. Automated detection of vehicles in aerial imagery is a challenging task, due to the density of vehicles on the road, the complexity of the surrounding environment in urban areas, and low spatial resolution...
Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle...
We take a look at current state of traffic sign classification discussing what makes it a specific problem of visual object classification. With impressive state-of-the-art results it is easy to forget that the domain extends beyond annotated datasets and overlook the problems that must be faced before we can start training classifiers. We discuss such problems, give an overview of previous work done,...
Among the human related factors, aggressive driving behavior is one of the major causes of traffic accidents [17]. On the other hand, detection and characterization of driver aggressiveness is a challenging task since there exist different psychological causes behind it. However, information about the driver behavior could be extracted from the data that is collected via different sensing devices...
Visual based approach has been studied extensively for on-road vehicle detection, while it faces great challenges, as visual appearance of a vehicle may change greatly across different viewpoints, and partial observation happens sometime due to occlusions from infrastructure or scene dynamics, and/or limited camera vision field. Inspired by the works on part-based detection, this research proposes...
As an important component of the driver assistance system or autonomous vehicle, traffic sign detection can provide drivers or vehicles with safety and alert information about the road. Most existing methods for traffic sign detection only focus on one or several categories of signs while there are various signs in the real world. This paper proposes a biologically-inspired method for detecting almost...
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...
Naturalistic driving studies (NDS) provide critical information about driving behaviors and characteristics that could lead to crashes and near-crashes. Such studies involve analysis of large volumes of data from multiple sensors and detection and extraction of critical events is an important step in NDS. This paper introduces techniques that analyze the visual data complemented with other sensors...
Precise localization is an essential issue for autonomous driving applications, where GPS-based systems are challenged to meet requirements such as lane-level accuracy. This paper introduces a new visual-based localization approach in dynamic traffic environments, focusing on and exploiting properties of structured roads like straight roads or intersections. Such environments show several line segments...
The idea presented in this paper is an online learning approach for behavior prediction of other road participants at an intersection. Learning traffic situations online has the advantage that it is possible to react to changes in driving behavior due to changes in the environment. If visual obstruction occurs because of changes in the environment, e.g. a growing corn field, the behavior of drivers...
Drivers are exposed to a growing risk of being distracted with the recent development of in-vehicle systems for navigation, communication and infotainment. As a result, there is a need for tracking systems that can monitor the drivers' attention. This study investigates driver distractions using a multimodal corpus collected from real world driving scenarios. The paper focuses on facial cues automatically...
GPS navigation is often found undependable in urban situations where tall structures occlude large parts of the sky. To keep accurate position in these situations, we need an alternative method. We propose a novel visual odometry method that is shown to provide reliable relative motion estimation in typical urban road driving using a single camera. While the short-term accuracy is good, relative motion...
Event detection is an important research in video surveillance technology. This paper proposed a method for traffic event detection based on visual Mechanism on the background of traffic video surveillance applications. In this method, based on the extraction of video target motion characteristics, it extracted abnormal targets mainly through the features merging and significant competitive in video...
Automatic analysis and understanding of common activities and detection of deviant behaviors is a challenging task in computer vision. This is particularly true in surveillance data, where busy traffic scenes are rich with multifarious activities many of them occurring simultaneously. In this paper, we address these issues with an unsupervised learning approach relying on probabilistic Latent Semantic...
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