5,462 research outputs found
Vehicles Recognition Using Fuzzy Descriptors of Image Segments
In this paper a vision-based vehicles recognition method is presented.
Proposed method uses fuzzy description of image segments for automatic
recognition of vehicles recorded in image data. The description takes into
account selected geometrical properties and shape coefficients determined for
segments of reference image (vehicle model). The proposed method was
implemented using reasoning system with fuzzy rules. A vehicles recognition
algorithm was developed based on the fuzzy rules describing shape and
arrangement of the image segments that correspond to visible parts of a
vehicle. An extension of the algorithm with set of fuzzy rules defined for
different reference images (and various vehicle shapes) enables vehicles
classification in traffic scenes. The devised method is suitable for
application in video sensors for road traffic control and surveillance systems.Comment: The final publication is available at http://www.springerlink.co
Fuzzy cellular model for on-line traffic simulation
This paper introduces a fuzzy cellular model of road traffic that was
intended for on-line applications in traffic control. The presented model uses
fuzzy sets theory to deal with uncertainty of both input data and simulation
results. Vehicles are modelled individually, thus various classes of them can
be taken into consideration. In the proposed approach, all parameters of
vehicles are described by means of fuzzy numbers. The model was implemented in
a simulation of vehicles queue discharge process. Changes of the queue length
were analysed in this experiment and compared to the results of NaSch cellular
automata model.Comment: The original publication is available at http://www.springerlink.co
Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems
The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous
vehicles must make responsible predictions in relation to surrounding
traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and
critical task is to explore the movement patterns of different traffic-agents
and predict their future trajectories accurately to help the autonomous vehicle
make reasonable navigation decision. To solve this problem, we propose a long
short-term memory-based (LSTM-based) realtime traffic prediction algorithm,
TrafficPredict. Our approach uses an instance layer to learn instances'
movements and interactions and has a category layer to learn the similarities
of instances belonging to the same type to refine the prediction. In order to
evaluate its performance, we collected trajectory datasets in a large city
consisting of varying conditions and traffic densities. The dataset includes
many challenging scenarios where vehicles, bicycles, and pedestrians move among
one another. We evaluate the performance of TrafficPredict on our new dataset
and highlight its higher accuracy for trajectory prediction by comparing with
prior prediction methods.Comment: Accepted by AAAI(Oral) 201
Distributed multi-class road user tracking in multi-camera network for smart traffic applications
Reliable tracking of road users is one of the important tasks in smart traffic applications. In these applications, a network of cameras is often used to extend the coverage. However, efficient usage of information from cameras which observe the same road user from different view points is seldom explored. In this paper, we present a distributed multi-camera tracker which efficiently uses information from all cameras with overlapping views to accurately track various classes of road users. Our method is designed for deployment on smart camera networks so that most computer vision tasks are executed locally on smart cameras and only concise high-level information is sent to a fusion node for global joint tracking. We evaluate the performance of our tracker on a challenging real-world traffic dataset in an aspect of Turn Movement Count (TMC) application and achieves high accuracy of 93%and 83% on vehicles and cyclist respectively. Moreover, performance testing in anomaly detection shows that the proposed method provides reliable detection of abnormal vehicle and pedestrian trajectories
Agreeing to Cross: How Drivers and Pedestrians Communicate
The contribution of this paper is twofold. The first is a novel dataset for
studying behaviors of traffic participants while crossing. Our dataset contains
more than 650 samples of pedestrian behaviors in various street configurations
and weather conditions. These examples were selected from approx. 240 hours of
driving in the city, suburban and urban roads. The second contribution is an
analysis of our data from the point of view of joint attention. We identify
what types of non-verbal communication cues road users use at the point of
crossing, their responses, and under what circumstances the crossing event
takes place. It was found that in more than 90% of the cases pedestrians gaze
at the approaching cars prior to crossing in non-signalized crosswalks. The
crossing action, however, depends on additional factors such as time to
collision (TTC), explicit driver's reaction or structure of the crosswalk.Comment: 6 pages, 6 figure
- …