The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we proposed a pedestrian detection system based on laser and image data fusion. The high speed of laser data based location and precise of image based classification are fully explored. First, laser scanner point data is clustered into segments, each of which implies a pedestrian candidate. Then, the segments are projected to the image domain to form regions of interest (ROI) on the...
Understanding driver behavior is critical towards ensuring superior levels of safety and environmental sustainability in intelligent transportation systems. Existing solutions for vital sign extraction are generally intrusive in that they affect the comfort of the driver and may consequently lead to biased observations. Moreover, low-complexity devices such as GPS receivers and the multitude of sensors...
A target selection method based on multi features fusion is proposed to improve the accuracy of target vehicle selection. The parameters consisting of the longitudinal distance, lateral distance, relative speed between objects and the host vehicle, the in-lane probability of objects are regarded as the features of individual vehicles. Firstly, some pre-processes of features data are carried out including...
Tracking of a moving ground target using acoustic signals obtained from a passive sensor network is a difficult problem as the signals are contaminated by wind noise and are hampered by road conditions, terrain and multipath, etc., and are not deterministic. Multiple target tracking becomes even more challenging, especially when some of the vehicles are light (wheeled) and some are heavy (e.g., tracked...
Vehicle classification is an important task for various traffic monitoring applications. This paper investigates the capabilities of acoustic feature generation for vehicle classification. Six temporal and spectral features are extracted from the audio recordings. Six different classification algorithms are compared using the extracted features. We focus on a single sensor setting to keep the computational...
We propose a robust system for multi-vehicle and multi-lane detection with integrating lane and vehicle information. Most research work only can detect the lanes or vehicles separately. However, the dependency between lane information and vehicle information are able to support each other achieving more reliable results. We use probabilistic data association filter to integrate the information of...
One of the important tasks in sensor networks is classifying moving vehicles. Fusion of large amount of sensor measurements can improve network performance and reduce the consumption of sensor network resource. We study using continuous measurements of multiple sensor nodes to improve the classification performance by spatio-temporal fusion and fault detection. Time series decisions of single sensor...
Today most so called `black spots' have been eliminated from the road networks. However, intersections can still be regarded as black spots. Depending on the region and country, from 30% to 60% of all injury accidents and up to one third of the fatalities occur at intersections. This is due mainly to the fact that accident scenarios at intersections are among the most complex ones, since different...
This paper describes a Takagi-Sugeno (T-S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Fuzzy Kalman filtering of the SLAM problem (FKF-SLAM) is used in this paper together with newly proposed data association algorithm. An extended TSDA (ETSDA) method is introduced for the SLAM problem in mobile robot navigation...
In this paper, we study the self lane assignment problem, i.e. given an image taken inside a vehicle, infer on which lane the image is taken. This problem serves as an example of active egocentric vision application with data fusion. In this application, a camera is mounted inside the vehicle looking outside to the world. Combined with a GPS with a digital map this smart mobile camera is capable of...
The last generation of autonomous underwater vehicles (AUV) is being developed with intervention capabilities in mind. These capabilities will provide easy and early inspection and maintenance of subsea offshore structures. The technology developments that implies this upgrade comprise new techniques for subsea vehicle localization, including detection and estimation algorithms. A reliable technique...
Despite its precise positioning performance, a GPS based navigation system may require the reference or augmentation station in close boundaries and is liable to be affected by satellite observation environments. Thus, this paper presents an INS/vision sensor integrated system, which in principle uses purely unknown feature points in previous epochs in order to cope with the limited GPS/INS integration...
Current research on acoustic vehicle classification has been generally aimed at utilizing various feature extraction methods and pattern recognition techniques. Previous research in gait biometrics has shown that domain knowledge or semantic enrichment can assist in improving the classification accuracy. In this paper, we address the problem of semantic enrichment by learning the semantic attributes...
Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion...
This paper presents an alternative formulation for the Bayesian feature-based simultaneous localisation and mapping (SLAM) problem, using a random finite set approach. For a feature based map, SLAM requires the joint estimation of the vehicle location and the map. The map itself involves the joint estimation of both the number of features and their states (typically in a 2D Euclidean space), as an...
This paper presents schemes to generate effective feature vectors of low dimension, and also presents a cluster-based algorithm, where sensors form clusters on-demand for the sake of running a classification task based on the produced feature vectors. The features generated through our proposed schemes are evaluated using k-nearest neighbor (k-NN) and maximum likelihood (ML) classifiers. The proposed...
The challenge of modern sensor systems is besides the tracking of targets more and more their classification. The knowledge of the target class has significant influence on the identification, threat evaluation and weapon assignment process of large systems. Especially, considering new types of threats in anti asymmetric warfare the knowledge of a target class has an important drawback. Also the target...
The aim of this paper is to present a multiple object tracking data fusion technique, which fuses radar, image, and ego vehicle odometry. The data are fused at a high level, which leads to reliable and stable tracking results providing also additional features as width estimation and the detection of stationary objects. A ldquorealrdquo application of these algorithms is illustrated on a specific...
Many acoustic factors can contribute to the classification accuracy of ground vehicles. Classification based on Acoustic information fusion for ground vehicle classification a single feature set may lose some useful information. To obtain more complete knowledge regarding vehiclespsila acoustic characteristics, we propose a fusion approach to combine two sets of features, in which various aspects...
This paper considers the problem of the classification of objects observed by vehicle embedded sensors. We propose a general architecture and an algorithm to perform multisensor fusion for the classification purpose. The proposed solution has to be robust and flexible. The robustness is essential because this system is for safety applications. The flexibility is ensured by a modular architecture alongside...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.