Real-time multiple moving vehicle detection and tracking framework for autonomous UAV monitoring of urban traffic

2013 ◽  
pp. ICTP 013
Author(s):  
Mohamed ElHelw
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Urban Traffic ◽  
Moving Vehicle ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Moving Vehicle Detection

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 Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Wei Sun ◽  
Min Sun ◽  
Xiaorui Zhang ◽  
Mian Li
Keyword(s):  
Optical Flow ◽  
Particle Filter ◽  
Vehicle Detection ◽  
Moving Vehicle ◽  
Flow Method ◽  
Optical Flow Method ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Particle Filter Algorithm ◽  
Moving Vehicle Detection

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

TRAFFIC VIDEO-BASED MOVING VEHICLE DETECTION AND TRACKING IN THE COMPLEX ENVIRONMENT

2009 ◽  
Vol 40 (7) ◽  
pp. 569-588 ◽  
Author(s):  
Tao Gao ◽  
Zheng-Guang Liu ◽  
Shi-Hong Yue ◽  
Jian-Qiang Mei ◽  
Jun Zhang
Keyword(s):  
Vehicle Detection ◽  
Complex Environment ◽  
Moving Vehicle ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Traffic Video ◽  
Moving Vehicle Detection

scholarly journals V-DaT: A Robust method for Vehicle Detection and Tracking

2021 ◽  
Vol 12 (2) ◽  
pp. 2492-2505
Author(s):  
Latha Anuj , Et. al.
Keyword(s):  
Background Subtraction ◽  
Vehicle Detection ◽  
Morphological Operations ◽  
Traffic Surveillance ◽  
Moving Vehicle ◽  
Directional Filtering ◽  
Detection And Tracking ◽  
Algorithmic Optimization ◽  
Vehicle Detection And Tracking ◽  
Moving Vehicle Detection

Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.

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