scholarly journals Robust Visual Tracking with Reliable Object Information and Kalman Filter

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 889
Author(s):  
Hang Chen ◽  
Weiguo Zhang ◽  
Danghui Yan
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Online Training ◽  
Linear Interpolation ◽  
Correlation Filter ◽  
Target Information ◽  
Filter Methods ◽  
Tracking Process ◽  
Background Clutter

Object information significantly affects the performance of visual tracking. However, it is difficult to obtain accurate target foreground information because of the existence of challenging scenarios, such as occlusion, background clutter, drastic change of appearance, and so forth. Traditional correlation filter methods roughly use linear interpolation to update the model, which may lead to the introduction of noise and the loss of reliable target information, resulting in the degradation of tracking performance. In this paper, we propose a novel robust visual tracking framework with reliable object information and Kalman filter (KF). Firstly, we analyze the reliability of the tracking process, calculate the confidence of the target information at the current estimated location, and determine whether it is necessary to carry out the online training and update step. Secondly, we also model the target motion between frames with a KF module, and use it to supplement the correlation filter estimation. Finally, in order to keep the most reliable target information of the first frame in the whole tracking process, we propose a new online training method, which can improve the robustness of the tracker. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of our proposed method, and our method achieves a comparable or better performance compared with several other state-of-the-art trackers.

scholarly journals Adaptive Context-Aware and Structural Correlation Filter for Visual Tracking

2019 ◽  
Vol 9 (7) ◽  
pp. 1338 ◽  
Author(s):  
Bin Zhou ◽  
Tuo Wang
Keyword(s):  
Visual Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Correlation Filter ◽  
Context Aware ◽  
Partial Occlusion ◽  
Structural Correlation ◽  
Background Clutter ◽  
Overall Performance ◽  
Fast Motion

Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, traditional CF-based trackers have insufficient context information, and easily drift in scenes of fast motion or background clutter. Moreover, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge. In this paper, we presented an adaptive context-aware (CA) and structural correlation filter for tracking. Firstly, we propose a novel context selecting strategy to obtain negative samples. Secondly, to gain robustness against partial occlusion, we construct a structural correlation filter by learning both the holistic and local models. Finally, we introduce an adaptive updating scheme by using a fluctuation parameter. Extensive comprehensive experiments on object tracking benchmark (OTB)-100 datasets demonstrate that our proposed tracker performs favorably against several state-of-the-art trackers.

scholarly journals Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

2021 ◽  
Author(s):  
Libin Xu ◽  
Pyoungwon Kim ◽  
Mengjie Wang ◽  
Jinfeng Pan ◽  
Xiaomin Yang ◽  
...  
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Boundary Effect ◽  
Correlation Filter ◽  
Star Model ◽  
Unified Framework ◽  
Alternating Direction ◽  
Background Clutter ◽  
Discriminative Correlation Filter ◽  
Spatio Temporal

AbstractThe discriminative correlation filter (DCF)-based tracking methods have achieved remarkable performance in visual tracking. However, the existing DCF paradigm still suffers from dilemmas such as boundary effect, filter degradation, and aberrance. To address these problems, we propose a spatio-temporal joint aberrance suppressed regularization (STAR) correlation filter tracker under a unified framework of response map. Specifically, a dynamic spatio-temporal regularizer is introduced into the DCF to alleviate the boundary effect and filter degradation, simultaneously. Meanwhile, an aberrance suppressed regularizer is exploited to reduce the interference of background clutter. The proposed STAR model is effectively optimized using the alternating direction method of multipliers (ADMM). Finally, comprehensive experiments on TC128, OTB2013, OTB2015 and UAV123 benchmarks demonstrate that the STAR tracker achieves compelling performance compared with the state-of-the-art (SOTA) trackers.

scholarly journals HKSiamFC: Visual-Tracking Framework Using Prior Information Provided by Staple and Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2137 ◽  
Author(s):  
Chenpu Li ◽  
Qianjian Xing ◽  
Zhenguo Ma
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
Prior Information ◽  
State Of The Art ◽  
The Other ◽  
Robust Tracking ◽  
Similarity Learning ◽  
Complex Environments ◽  
Color Information ◽  
Learning Problem

In the field of visual tracking, trackers based on a convolutional neural network (CNN) have had significant achievements. The fully-convolutional Siamese (SiamFC) tracker is a typical representation of these CNN trackers and has attracted much attention. It models visual tracking as a similarity-learning problem. However, experiments showed that SiamFC was not so robust in some complex environments. This may be because the tracker lacked enough prior information about the target. Inspired by the key idea of a Staple tracker and Kalman filter, we constructed two more models to help compensate for SiamFC’s disadvantages. One model contained the target’s prior color information, and the other the target’s prior trajectory information. With these two models, we design a novel and robust tracking framework on the basis of SiamFC. We call it Histogram–Kalman SiamFC (HKSiamFC). We also evaluated HKSiamFC tracker’s performance on dataset of the online object tracking benchmark (OTB) and Temple Color (TC128), and it showed quite competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

2014 ◽  
Vol 1037 ◽  
pp. 373-377 ◽  
Author(s):  
Teng Fei ◽  
Liu Qing ◽  
Lin Zhu ◽  
Jing Li
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Closed Circuit ◽  
Video Sequences ◽  
Inland Waterway ◽  
Cluttered Background ◽  
Full Occlusion

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

scholarly journals Real-Time Visual Tracking with Variational Structure Attention Network

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4904 ◽  
Author(s):  
Yeongbin Kim ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Boundary Effect ◽  
Online Training ◽  
Correlation Filter ◽  
Shape Distortion ◽  
Correlation Filters ◽  
Attention Network ◽  
Real Time Processing ◽  
Variational Structure

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.

scholarly journals An Improved Kernelized Correlation Filter Based Visual Tracking Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianjun Ni ◽  
Xue Zhang ◽  
Pengfei Shi ◽  
Jinxiu Zhu
Keyword(s):  
Visual Tracking ◽  
Template Matching ◽  
Visual Target ◽  
State Of The Art ◽  
Correlation Filter ◽  
Matching Method ◽  
Tracking Method ◽  
Template Update ◽  
Update Strategy ◽  
Visual Target Tracking

Correlation filter based trackers have received great attention in the field of visual target tracking, which have shown impressive advantages in terms of accuracy, robustness, and speed. However, there are still some challenges that exist in the correlation filter based methods, such as target scale variation and occlusion. To deal with these problems, an improved kernelized correlation filter (KCF) tracker is proposed, by employing the GM(1,1) grey model, the interval template matching method, and multiblock scheme. In addition, a strict template update strategy is presented in the proposed method to accommodate the appearance change and avoid template corruption. Finally, some experiments are conducted. The proposed method is compared with the top state-of-the-art trackers, and all the tracking algorithms are evaluated on the object tracking benchmark. The experimental results demonstrate obvious improvements of the proposed KCF-based visual tracking method.

scholarly journals Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting

2021 ◽  
Vol 11 (3) ◽  
pp. 953
Author(s):  
Jin Hong ◽  
Junseok Kwon
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Tracking Method ◽  
Aerial Vehicles ◽  
Object Proposal ◽  
Background Clutter ◽  
Art Performance ◽  
Over Time

In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to exist. The proposed object proposal method is robust to small objects and severe background clutter. For this, we vote on candidate areas of the object and increase or decrease the weight of the area accordingly. Thus, the method can accurately propose the object areas that can be used to track small-sized UAVs with the assumption that their motion is smooth over time. Experimental results verify that UAVs are accurately tracked even when they are very small and the background is complex. The proposed method qualitatively and quantitatively delivers state-of-the-art performance in comparison with conventional object proposal-based methods.

scholarly journals Robust Visual Tracking Using Structural Patch Response Map Fusion Based on Complementary Correlation Filter and Color Histogram

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4178 ◽  
Author(s):  
Zhaohui Hao ◽  
Guixi Liu ◽  
Jiayu Gao ◽  
Haoyang Zhang
Keyword(s):  
Full Advantage ◽  
Visual Tracking ◽  
State Of The Art ◽  
Color Histogram ◽  
Correlation Filter ◽  
Illumination Variation ◽  
Representation Model ◽  
Two Component

A part-based strategy has been applied to visual tracking with demonstrated success in recent years. Different from most existing part-based methods that only employ one type of tracking representation model, in this paper, we propose an effective complementary tracker based on structural patch response fusion under correlation filter and color histogram models. The proposed method includes two component trackers with complementary merits to adaptively handle illumination variation and deformation. To identify and take full advantage of reliable patches, we present an adaptive hedge algorithm to hedge the responses of patches into a more credible one in each component tracker. In addition, we design different loss metrics of tracked patches in two components to be applied in the proposed hedge algorithm. Finally, we selectively combine the two component trackers at the response maps level with different merging factors according to the confidence of each component tracker. Extensive experimental evaluations on OTB2013, OTB2015, and VOT2016 datasets show outstanding performance of the proposed algorithm contrasted with some state-of-the-art trackers.

scholarly journals Robust Scale Adaptive Visual Tracking with Correlation Filters

2018 ◽  
Vol 8 (11) ◽  
pp. 2037 ◽  
Author(s):  
Chunbao Li ◽  
Bo Yang
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Estimation Method ◽  
Color Naming ◽  
Target Object ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Object Proposals ◽  
Benchmark Datasets ◽  
Candidate Object

Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.

Sign in / Sign up

Export Citation Format

Share Document