scholarly journals Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 9380-9407 ◽  
Author(s):  
Lei Qin ◽  
Hichem Snoussi ◽  
Fahed Abdallah
Keyword(s):  
Object Tracking ◽  
Model Updating ◽  
Visual Surveillance ◽  
Covariance Descriptor

scholarly journals Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2046 ◽  
Author(s):  
Md Islam ◽  
Guoqing Hu ◽  
Qianbo Liu
Keyword(s):  
Object Tracking ◽  
Model Updating ◽  
Learning Rate ◽  
Dynamic Learning

scholarly journals A Fast Object Tracker Based on Integrated Multiple Features and Dynamic Learning Rate

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Jianming Zhang ◽  
You Wu ◽  
Xiaokang Jin ◽  
Feng Li ◽  
Jin Wang
Keyword(s):  
Object Tracking ◽  
Large Scale ◽  
Model Updating ◽  
Color Naming ◽  
Learning Rate ◽  
Appearance Model ◽  
Dynamic Learning ◽  
Tracking Algorithms ◽  
Single Feature ◽  
Confidence Degree

Object tracking is a vital topic in computer vision. Although tracking algorithms have gained great development in recent years, its robustness and accuracy still need to be improved. In this paper, to overcome single feature with poor representation ability in a complex image sequence, we put forward a multifeature integration framework, including the gray features, Histogram of Gradient (HOG), color-naming (CN), and Illumination Invariant Features (IIF), which effectively improve the robustness of object tracking. In addition, we propose a model updating strategy and introduce a skewness to measure the confidence degree of tracking result. Unlike previous tracking algorithms, we judge the relationship of skewness values between two adjacent frames to decide the updating of target appearance model to use a dynamic learning rate. This way makes our tracker further improve the robustness of tracking and effectively prevents the target drifting caused by occlusion and deformation. Extensive experiments on large-scale benchmark containing 50 image sequences show that our tracker is better than most existing excellent trackers in tracking performance and can run at average speed over 43 fps.

Corrected Background-weighted Histogram Mean Shift Tracking Algorithm Based on Adaptive Bandwidth

2013 ◽  
Vol 401-403 ◽  
pp. 1543-1546
Author(s):  
Feng Liu ◽  
Chao Zhang ◽  
Xiao Pei Wu
Keyword(s):  
Probability Distribution ◽  
Object Tracking ◽  
Model Updating ◽  
Mean Shift ◽  
Spatial Localization ◽  
Target Localization ◽  
Invariant Moment ◽  
Adaptive Kernel ◽  
Weighted Model ◽  
Weighted Histogram

The CBWH (corrected background-weighted histogram) scheme can effectively reduce backgrounds interference in target localization. But it still has the problem of scale and spatial localization inaccuracy. To solve the above issues, we proposed a method which generates a color probability distribution by taking advantage of the targets salient features. In the binary image, we calculate the invariant moment and thus to resize the tracking window of the next frame. A simple background-weighted model updating method is adopted to adapt to the complex background in tracking. Experimental results show that the proposed algorithm improves the robustness of object tracking by self-adaptive kernel-bandwidth updating.

Object tracking via collaborative multi-task learning and appearance model updating

2015 ◽  
Vol 31 ◽  
pp. 81-90 ◽  
Author(s):  
Xu Cheng ◽  
Nijun Li ◽  
Tongchi Zhou ◽  
Lin Zhou ◽  
Zhenyang Wu
Keyword(s):  
Object Tracking ◽  
Model Updating ◽  
Appearance Model ◽  
Task Learning

Multiple Object Detection and Tracking in Complex Background

2017 ◽  
Vol 31 (02) ◽  
pp. 1755003 ◽  
Author(s):  
Yingdong Ma ◽  
Yuchen Liu ◽  
Shuai Liu ◽  
Zhibin Zhang
Keyword(s):  
Object Tracking ◽  
Model Updating ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Multiple Targets ◽  
Detection And Tracking ◽  
Multiple Object ◽  
Complex Background ◽  
Tracking Objects ◽  
Multiple Particle

Multiple object tracking is a fundamental step for many computer vision applications. However, detecting and tracking objects in complex background is still a challenging task. This paper proposes an approach, which combines an improved Gaussian mixture modeling (GMM) with multiple particle filters (MPFs) for automatic multiple targets detecting and tracking. For GMM, we make improvement on GMM in the phase of model updating by using the expectation maximization algorithm and [Formula: see text] recent frames with weight parameters of Gaussian distributions. In the tracking stage, we integrate multiple features of targets, including color, edge and depth, into MPFs to improve the performance of object tracking. By comparing with various particle filter approaches, the experimental results show that our approach can track multiple targets in complex backgrounds automatically and accurately.

RECENT ADVANCE ON MEAN SHIFT TRACKING: A SURVEY

2013 ◽  
Vol 13 (03) ◽  
pp. 1350012 ◽  
Author(s):  
LIWEN HE ◽  
YONG XU ◽  
YAN CHEN ◽  
JIAJUN WEN
Keyword(s):  
Object Tracking ◽  
Model Updating ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
The Past ◽  
Tracking Algorithms ◽  
On Line ◽  
The Mean ◽  
Working Procedure ◽  
Smart Rooms

Though there have been many applications of object tracking, ranging from surveillance and monitoring to smart rooms, object tracking is always a challenging problem in computer vision over the past decades. Mean Shift-based object tracking has received much attention because it has a great number of advantages over other object tracking algorithms, e.g. real time, robust and easy to implement. In this survey, we first introduce the basic principle of the Mean Shift algorithm and the working procedure using the Mean Shift algorithm to track the object. This paper then describes the defects and potential issues of the traditional Mean Shift algorithm. Finally, we summarize the improvements to the Mean Shift algorithm and some hybrid tracking algorithms that researchers have proposed. The main improvements include scale adaptation, kernel selection, on-line model updating, feature selection and mode optimization, etc.

scholarly journals Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2751 ◽  
Author(s):  
Xizhe Xue ◽  
Ying Li ◽  
Qiang Shen
Keyword(s):  
Object Tracking ◽  
Model Updating ◽  
Low Cost ◽  
Traffic Monitoring ◽  
Appearance Model ◽  
Viewpoint Change ◽  
Aerial Vehicle ◽  
Ratio Change ◽  
The Stability ◽  
New Applications

With the increasing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), visual tracking using UAVs has become more and more important due to its many new applications, including automatic navigation, obstacle avoidance, traffic monitoring, search and rescue, etc. However, real-world aerial tracking poses many challenges due to platform motion and image instability, such as aspect ratio change, viewpoint change, fast motion, scale variation and so on. In this paper, an efficient object tracking method for UAV videos is proposed to tackle these challenges. We construct the fused features to capture the gradient information and color characteristics simultaneously. Furthermore, cellular automata is introduced to update the appearance template of target accurately and sparsely. In particular, a high confidence model updating strategy is developed according to the stability function. Systematic comparative evaluations performed on the popular UAV123 dataset show the efficiency of the proposed approach.

Occlusion management in distributed multi-object tracking for visual-surveillance

2015 ◽  
Vol 25 (2) ◽  
pp. 295-300 ◽  
Author(s):  
F. Bousetouane ◽  
F. Vandewiele ◽  
C. Motamed
Keyword(s):  
Object Tracking ◽  
Visual Surveillance
Sign in / Sign up

Export Citation Format

Share Document