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.

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

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

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.

scholarly journals Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1129 ◽  
Author(s):  
Jianming Zhang ◽  
Yang Liu ◽  
Hehua Liu ◽  
Jin Wang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Correlation Filter ◽  
Appearance Model ◽  
Visual Object ◽  
Multiple Correlation ◽  
Correlation Filters ◽  
Tracking Algorithms ◽  
Global Correlation ◽  
Object Appearance

Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.

scholarly journals Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
Bineng Zhong ◽  
Shengnan Pan ◽  
Cheng Wang ◽  
Tian Wang ◽  
Jixiang Du ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Large Scale ◽  
Individual Cell ◽  
Fundamental Problem ◽  
Feature Learning ◽  
Principal Component ◽  
Feature Representation ◽  
Appearance Model ◽  
Extrinsic Factors

Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of a target by exploiting both classic principal component analysis (PCA) algorithms with recent deep learning representation architectures. We use learned PCA eigenvectors as filters and develop a novel algorithm to represent a target by composing of a PCA-based filter bank layer, a nonlinear layer, and a patch-based pooling layer, respectively. Then, based on the feature representation, a neural network with one hidden layer is trained in a supervised mode to construct a discriminative appearance model. Finally, to alleviate the tracker drifting problem, a sample update scheme is carefully designed to keep track of the most representative and diverse samples during tracking. We test the proposed tracking method on two standard individual cell/object tracking benchmarks to show our tracker's state-of-the-art performance.

scholarly journals Self-balance Motion and Appearance Model for Multi-object Tracking in UAV

2019 ◽  
Author(s):  
Hongyang Yu ◽  
Guorong Li ◽  
Weigang Zhang ◽  
Hongxun Yao ◽  
Qingming Huang
Keyword(s):  
Object Tracking ◽  
Appearance Model

scholarly journals Adaptive Channel Selection for Robust Visual Object Tracking with Discriminative Correlation Filters

2021 ◽  
Author(s):  
Tianyang Xu ◽  
Zhenhua Feng ◽  
Xiao-Jun Wu ◽  
Josef Kittler
Keyword(s):  
Object Tracking ◽  
Augmented Lagrangian Method ◽  
Channel Selection ◽  
Image Feature ◽  
Superior Performance ◽  
Appearance Model ◽  
Visual Object ◽  
Correlation Filters ◽  
Visual Object Tracking ◽  
Feature Representations

AbstractDiscriminative Correlation Filters (DCF) have been shown to achieve impressive performance in visual object tracking. However, existing DCF-based trackers rely heavily on learning regularised appearance models from invariant image feature representations. To further improve the performance of DCF in accuracy and provide a parsimonious model from the attribute perspective, we propose to gauge the relevance of multi-channel features for the purpose of channel selection. This is achieved by assessing the information conveyed by the features of each channel as a group, using an adaptive group elastic net inducing independent sparsity and temporal smoothness on the DCF solution. The robustness and stability of the learned appearance model are significantly enhanced by the proposed method as the process of channel selection performs implicit spatial regularisation. We use the augmented Lagrangian method to optimise the discriminative filters efficiently. The experimental results obtained on a number of well-known benchmarking datasets demonstrate the effectiveness and stability of the proposed method. A superior performance over the state-of-the-art trackers is achieved using less than $$10\%$$ 10 % deep feature channels.

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