An OPS Hand Tracking Algorithm Based on Optical Flow

2009 ◽  
Author(s):  
Zhou Hang
Keyword(s):  
Optical Flow ◽  
Tracking Algorithm ◽  
Hand Tracking

Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

2014 ◽  
Vol 687-691 ◽  
pp. 564-571 ◽  
Author(s):  
Lin Bao Xu ◽  
Shu Ming Tang ◽  
Jin Feng Yang ◽  
Yan Min Dong
Keyword(s):  
Optical Flow ◽  
Fast Moving ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Robust Tracking ◽  
Symmetry Detection ◽  
Tracking Method ◽  
Robot Tracking ◽  
Improve Accuracy ◽  
First Time

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

Poster - Thur Eve - 71: Evaluation of a Breathing Phantom's Tumour Motion Using Portal Images and an Optical Flow Tracking Algorithm

2010 ◽  
Vol 37 (7Part3) ◽  
pp. 3900-3901
Author(s):  
TP-T Teo ◽  
RN Crow ◽  
D Sasaki ◽  
S Pistorius
Keyword(s):  
Optical Flow ◽  
Tracking Algorithm ◽  
Portal Images

scholarly journals Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos

2019 ◽  
Vol 11 (19) ◽  
pp. 2278
Author(s):  
Tao Yang ◽  
Dongdong Li ◽  
Yi Bai ◽  
Fangbing Zhang ◽  
Sen Li ◽  
...  
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Target Tracking ◽  
Multiple Object Tracking ◽  
Tracking Algorithm ◽  
Frame Rate ◽  
Dense Optical Flow ◽  
Multiple Object ◽  
Dense Trajectory ◽  
Low Frame Rate

In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.

A real-time multi-cue hand tracking algorithm based on computer vision

2010 ◽  
Author(s):  
Zhigeng Pan ◽  
Yang Li ◽  
Mingmin Zhang ◽  
Chao Sun ◽  
Kangde Guo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Tracking Algorithm ◽  
Hand Tracking

scholarly journals Application of Optical Flow Technique and Photogrammetry for Rockfall Dynamics: A Case Study on a Field Test

2021 ◽  
Vol 13 (20) ◽  
pp. 4124
Author(s):  
Dong-Hyun Kim ◽  
Ivan Gratchev
Keyword(s):  
Optical Flow ◽  
Dynamic Behavior ◽  
Monitoring System ◽  
Field Test ◽  
Current Approach ◽  
Tracking Algorithm ◽  
Motion Data ◽  
Slope Monitoring

Optical flow is a vision-based approach that is used for tracking the movement of objects. This robust technique can be an effective tool for determining the source of failures on slope surfaces, including the dynamic behavior of rockfall. However, optical flow-based measurement still remains an issue as the data from optical flow algorithms can be affected by the varied photographing environment, such as weather and illuminations. To address such problems, this paper presents an optical flow-based tracking algorithm that can be employed to extract motion data from video records for slope monitoring. Additionally, a workflow combined with photogrammetry and the optical flow technique has been proposed for producing highly accurate estimations of rockfall motion. The effectiveness of the proposed approach has been evaluated with the dataset obtained from a photogrammetry survey of field rockfall tests performed by the authors in 2015. The results show that the workflow adopted in this study can be suitable to identify rockfall events overtime in a slope monitoring system. The limitations of the current approach are also discussed.

Development of the human face tracking algorithm based on the optical flow application

2018 ◽  
Vol 10 (5) ◽  
pp. 86-101
Author(s):  
A.A. Druki ◽  
V.G. Spitsyn ◽  
Yu.A. Bolotova ◽  
D. Oliva ◽  
A.V. Gelginberg
Keyword(s):  
Optical Flow ◽  
Face Tracking ◽  
Tracking Algorithm ◽  
Human Face

Brain Nerve Fiber Tracking Algorithm Based on Improved Optical Flow in Combination of Bayesian Prior Constraints

2020 ◽  
Vol 10 (2) ◽  
pp. 452-457
Author(s):  
Shen Jian ◽  
Chen Huan ◽  
Zuo Jianjian ◽  
Pan Xuming
Keyword(s):  
Optical Flow ◽  
Nerve Fiber ◽  
Diffusion Tensor ◽  
Three Dimensional ◽  
Fiber Tracking ◽  
Nerve Fibers ◽  
Tracking Algorithm ◽  
Brain Diseases ◽  
Field Estimation ◽  
The Brain

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) can track the brain nerve fiber and reconstruct non-invasively the three-dimensional image by tracing the local tensor orientation. The commonly used tracking method is usually based on the local diffusion information and insufficient to consider the geometrical structure and fractional anisotropy which is constrained by anatomical structure and physiological function of human. Therefore, a novel brain nerve fiber tracking algorithm based on Bayesian optical-flow constrained framework is proposed. The construction of energy function is the core step of global optical flow field estimation technology. In this paper, data fidelity constraint, prior constraint, penalty function and weight factor are introduced to construct Bayesian constraint function. The fiber trend model is displayed intuitively to obtain the structure and direction of the inner nerve fibers of the brain, which can better assist in the diagnosis and treatment of clinical brain diseases, and lay a foundation for subsequent brain tissue research.

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