ROBUST AND FAST TRACKING ALGORITHM IN VIDEO SEQUENCES BY ADAPTIVE WINDOW SIZING USING A NOVEL ANALYSIS ON SPATIOTEMPORAL GRADIENT POWERS

2007 ◽  
Vol 16 (02) ◽  
pp. 305-317 ◽  
Author(s):  
PAYMAN MOALLEM ◽  
ALIREZA MEMARMOGHADDAM ◽  
MOHSEN ASHOURIAN
Keyword(s):  
Window Size ◽  
Image Sequences ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Occupancy Rate ◽  
Tracking Method ◽  
Fast Tracking ◽  
Adaptive Window ◽  
Real Video ◽  
Adaptively Adjusting

Success of a tracking method depends largely on choosing the suitable window size as soon as the target size changes in image sequences. To achieve this goal, we propose a fast tracking algorithm based on adaptively adjusting tracking window. Firstly, tracking window is divided into four edge subwindows, and a background subwindow around it. Then, by calculating the spatiotemporal gradient power ratios of the target in each subwindow, four proper expansion vectors are associated with any tracking window sides such that the occupancy rate of the target in tracking window should be maintained within a specified range. In addition, since temporal changing of target is evaluated in calculating these vectors, we estimate overall target displacement by sum of expansion vectors. Experimental results using various real video sequences show that the proposed algorithm successfully track an unknown textured target in real time, and is robust to dynamic occlusions in complex noisy backgrounds.

scholarly journals Object Tracking via 2DPCA andl2-Regularization

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Haijun Wang ◽  
Hongjuan Ge ◽  
Shengyan Zhang
Keyword(s):  
Bayesian Inference ◽  
Object Tracking ◽  
Likelihood Function ◽  
State Of The Art ◽  
Reconstruction Error ◽  
Image Sequences ◽  
Projection Matrix ◽  
Tracking Method ◽  
Fast Tracking ◽  
Bayesian Inference Framework

We present a fast and robust object tracking algorithm by using 2DPCA andl2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt thel2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.

An adaptive window object tracking algorithm based on variable resolution

2011 ◽  
Vol 19 (2) ◽  
Author(s):  
L. Li
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Posterior Probability ◽  
Sampling Method ◽  
Tracking Algorithm ◽  
Variable Resolution ◽  
Tracking Method ◽  
Adaptive Window ◽  
Maximum Posterior Probability ◽  
Probability Index

AbstractThis paper presents an adaptive window object tracking method based on variable resolution. It copes with the change in size of the object during visual tracking. On the basis of the visual tracking algorithm, based on maximum posterior probability, we analyze the posterior probability index on the inside and outside panes of the object window, then build a mathematical model for adjusting object size with an adaptive window. Since the resolution changes according to the size of the object, this thesis uses a statistical sampling method of the feature by variable resolution. The resolution of the statistical feature is correspondingly changed in object tracking with an adaptive window. The resolution of a larger object is decreased, which realizes an object tracking method with adaptive window based on variable resolution.

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.

Image-Guided Transapical Aortic Valve Implantation Sensorless Tracking of Stenotic Valve Landmarks in Live Fluoroscopic Images

2011 ◽  
Vol 6 (4) ◽  
pp. 231-236 ◽  
Author(s):  
Denis R. Merk ◽  
Mohamed Esmail Karar ◽  
Claire Chalopin ◽  
David Holzhey ◽  
Volkmar Falk ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Contrast Agent ◽  
Image Sequences ◽  
Invasive Technique ◽  
Valve Prosthesis ◽  
Tracking Method ◽  
Fluoroscopic Image ◽  
Aortic Valve Implantation ◽  
Valve Implantation ◽  
Contrast Image

Objective Aortic valve stenosis is one of the most frequently acquired valvular heart diseases, accounting for almost 70% of valvular cardiac surgery. Transapical transcatheter aortic valve implantation has recently become a suitable minimally invasive technique for high-risk and elderly patients with severe aortic stenosis. In this article, we aim to automatically define a target area of valve implantation, namely, the area between the coronary ostia and the lowest points of two aortic valve cusps. Therefore, we present a new image-based tracking method of these aortic landmarks to assist in the placement of aortic valve prosthesis under live 2D fluoroscopy guidance. Methods We propose a rigid intensity-based image registration technique for tracking valve landmarks in 2D fluoroscopic image sequences, based on a real-time alignment of a contrast image including the initialized manual valve landmarks to each image of sequence. The contrast image is automatically detected to visualize aortic valve features when the aortic root is filled with a contrast agent. Results Our registration-based tracking method has been retrospectively applied to 10 fluoroscopic image sequences from routine transapical aortic valve implantation procedures. Most of all tested fluoroscopic images showed a successful tracking of valve landmarks, especially for the images without contrast agent injections. Conclusions A new intraoperative image-based method has been developed for tracking aortic valve landmarks in live 2D fluoroscopic images to assist transapical aortic valve implantations and to increase the overall safety of surgery as well.

scholarly journals Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Hui Li ◽  
Yun Liu ◽  
Chuanxu Wang ◽  
Shujun Zhang ◽  
Xuehong Cui
Keyword(s):  
Particle Filters ◽  
A Priori ◽  
Texture Features ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Particle State ◽  
Pedestrian Tracking ◽  
Estimation Problems ◽  
Non Gaussian ◽  
Gaussian Estimation

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.

scholarly journals Long-term target tracking combined with re-detection

2020 ◽  
Author(s):  
Juanjuan Wang ◽  
HaoRan Yang ◽  
Ning Xu ◽  
Chengqin Wu ◽  
ZengShun Zhao ◽  
...  
Keyword(s):  
Correlation Energy ◽  
State Of The Art ◽  
Tracking Algorithm ◽  
Correlation Filters ◽  
Short Term ◽  
Tracking Method ◽  
Tracking Tasks ◽  
Svm Model ◽  
Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

scholarly journals Anti-Interference Aircraft-Tracking Method in Infrared Imagery

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1289
Author(s):  
Sijie Wu ◽  
Kai Zhang ◽  
Saisai Niu ◽  
Jie Yan
Keyword(s):  
Infrared Imaging ◽  
Comprehensive Evaluation ◽  
Infrared Image ◽  
Image Sequences ◽  
Tracking Performance ◽  
Infrared Imagery ◽  
Tracking Method ◽  
Tracking Process ◽  
Aircraft Tracking ◽  
Infrared Signature

In this paper, we focus on developing an algorithm for infrared-imaging guidance that enables the aircraft to be reliably tracked in the event of interference. The key challenge is to track the aircraft with occlusion caused by decoys and drastic appearance changes resulting from a diversity of attacking angles. To address this challenge, an aircraft-tracking algorithm was proposed, which provides robustness in tracking the aircraft against the decoys. We reveal the inherent structure and infrared signature of the aircraft, which are used as discriminative features to track the aircraft. The anti-interference method was developed based on simulated images but validate the effectiveness on both real infrared image sequences without decoys and simulated infrared imagery. For frequent occlusion caused by the decoys, the mechanism of occlusion detection is exploited according to the variation of the model distance in tracking process. To have a comprehensive evaluation of tracking performance, infrared-image sequences with different attack angles were simulated, and experiments on benchmark trackers were performed to quantitatively evaluate tracking performance. The experiment results demonstrate that our aircraft-tracking method performs favorably against state-of-the-art trackers.

scholarly journals Partial Shading Detection and Global Maximum Power Point Tracking Algorithm for Photovoltaic with the Variation of Irradiation and Temperature

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 202 ◽  
Author(s):  
Jirada Gosumbonggot ◽  
Goro Fujita
Keyword(s):  
Maximum Power Point Tracking ◽  
Tracking Algorithm ◽  
Maximum Power ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Tracking Method ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Photovoltaic (PV) technology has been the focus of interest due to its nonpolluting operation and good installation flexibility. Irradiation and temperature are the two main factors which impact the performance of the PV system. Accordingly, when partial shading from surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Since the conventional maximum power point tracking methods (MPPT) could not distinguish the global maximum power of the power-voltage (P-V) characteristic curve, a new tracking method needs to be developed. This paper proposes a global maximum power point tracking method using shading detection and the trend of slopes from each section of the curve. Full mathematical equations and algorithms are presented. Simulations based on real weather data were performed both in short-term and long-term studies. Moreover, this paper also presents the experiment using the DC-DC synchronous and interleaved boost converter. Results from the simulation show an accurate tracking result and the system can enhance the total energy generated by 8.55% compared to the conventional scanning method. Moreover, the experiment also confirms the success of the proposed tracking algorithm.

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