Annealed Particle Filter Algorithm Used for Lane Detection and Tracking

2013 ◽  
Vol 1 (1) ◽  
pp. 31-35 ◽  
Author(s):  
Hongying Zhao ◽  
Zhu Teng ◽  
Hong-Hyun Kim ◽  
Dong-Joong Kang
Keyword(s):  
Particle Filter ◽  
Lane Detection ◽  
Detection And Tracking ◽  
Particle Filter Algorithm ◽  
Annealed Particle Filter

Lane detection and tracking based on annealed particle filter

2014 ◽  
Vol 12 (6) ◽  
pp. 1303-1312 ◽  
Author(s):  
Hongying Zhao ◽  
Onecue Kim ◽  
Jong-Seob Won ◽  
Dong-Joong Kang
Keyword(s):  
Particle Filter ◽  
Lane Detection ◽  
Detection And Tracking ◽  
Annealed Particle Filter

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.

scholarly journals Flying object tracking and classification of military versus nonmilitary aircraft

2020 ◽  
Vol 9 (4) ◽  
pp. 1394-1403
Author(s):  
Ehsan Akbari Sekehravani ◽  
Eduard Babulak ◽  
Mehdi Masoodi
Keyword(s):  
Machine Vision ◽  
Particle Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Center Of Mass ◽  
Speed Of Processing ◽  
Detection And Tracking ◽  
Particle Filter Algorithm ◽  
Tracking Objects

Tracking of moving objects in a sequence of images is one of the important and functional branches of machine vision technology. Detection and tracking of a flying object with unknown features are important issues in detecting and tracking objects. This paper consists of two basic parts. The first part involves tracking multiple flying objects. At first, flying objects are detected and tracked, using the particle filter algorithm. The second part is to classify tracked objects (military or nonmilitary), based on four criteria; Size (center of mass) of objects, object speed vector, the direction of motion of objects, and thermal imagery identifies the type of tracked flying objects. To demonstrate the efficiency and the strength of the algorithm and the above system, several scenarios in different videos have been investigated that include challenges such as the number of objects (aircraft), different paths, the diverse directions of motion, different speeds and various objects. One of the most important challenges is the speed of processing and the angle of imaging.

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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