scholarly journals Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios

2017 ◽  
Author(s):  
Victor Vaquero ◽  
Ivan del Pino ◽  
Francese Moreno-Noguer ◽  
Joan Sola ◽  
Alberto Sanfeliu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Vehicle Detection ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking

scholarly journals Real-time multiple vehicle detection and tracking from a moving vehicle

2000 ◽  
Vol 12 (2) ◽  
pp. 69-83 ◽  
Author(s):  
Margrit Betke ◽  
Esin Haritaoglu ◽  
Larry S. Davis
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Moving Vehicle ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking

scholarly journals Vehicle pose estimation for vehicle detection and tracking based on road direction

2017 ◽  
Vol 3 (1) ◽  
pp. 35
Author(s):  
Adhi Prahara ◽  
Ahmad Azhari ◽  
Murinto Murinto
Keyword(s):  
Vehicle Detection ◽  
Training Data ◽  
Support Vector ◽  
Traffic Surveillance ◽  
Vehicle Performance ◽  
Surveillance Camera ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Different Color ◽  
Linear Svm

Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC).

Microscopic Traffic Data Collection by Remote Sensing

2003 ◽  
Vol 1855 (1) ◽  
pp. 121-128 ◽  
Author(s):  
S. P. Hoogendoorn ◽  
H. J. Van Zuylen ◽  
M. Schreuder ◽  
B. Gorte ◽  
G. Vosselman
Keyword(s):  
Data Collection ◽  
Digital Camera ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Lateral Position ◽  
Aerial Images ◽  
Detection And Tracking ◽  
Vehicle Trajectories ◽  
Vehicle Detection And Tracking ◽  
Traffic Data Collection

To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining individual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good; this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.

Road boundary estimation to improve vehicle detection and tracking in UAV video

2014 ◽  
Vol 21 (12) ◽  
pp. 4732-4741 ◽  
Author(s):  
Li-ye Zhang ◽  
Zhong-ren Peng ◽  
Li Li ◽  
Hua Wang
Keyword(s):  
Vehicle Detection ◽  
Detection And Tracking ◽  
Boundary Estimation ◽  
Vehicle Detection And Tracking

Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR

2019 ◽  
Vol 2673 (9) ◽  
pp. 62-71 ◽  
Author(s):  
Zhenyao Zhang ◽  
Jianying Zheng ◽  
Hao Xu ◽  
Xiang Wang
Keyword(s):  
High Resolution ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Vehicle Detection ◽  
Traffic Data ◽  
The Road ◽  
Detection And Tracking ◽  
Area Of Interest ◽  
Vehicle Detection And Tracking ◽  
On The Road

The problem of traffic safety has become increasingly prominent owing to the increase in the number of cars. Traffic accidents often occur in an instant, which makes it necessary to obtain traffic data with high resolution. High-resolution micro traffic data (HRMTD) indicates that the spatial resolution reaches the centimeter level and that the temporal resolution reaches the millisecond level. The position, direction, speed, and acceleration of objects on the road can be extracted with HRMTD. In this paper, a LiDAR sensor was installed at the roadside for data collection. An adjacent-frame fusion method for vehicle detection and tracking in complex traffic circumstances is presented. Compared with the previous research, objects can be detected and tracked without object model extraction or a bounding box description. In addition, problems caused by occlusion can be improved using adjacent frames fusion in the vehicle detection and tracking algorithms in this paper. The data processing procedure are as follows: selection of area of interest, ground point removal, vehicle clustering, and vehicle tracking. The algorithm has been tested at different sites (in Reno and Suzhou), and the results demonstrate that the algorithm can perform well in both simple and complex application scenarios.

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