Multi-Templates Based Robust Tracking for Robot Person-Following Tasks

Minghe Cao; Jianzhong Wang; Li Ming

doi:10.3390/app11188698

Multi-Templates Based Robust Tracking for Robot Person-Following Tasks

Applied Sciences ◽

10.3390/app11188698 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8698

Author(s):

Minghe Cao ◽

Jianzhong Wang ◽

Li Ming

Keyword(s):

Visual Tracking ◽

Cross Correlation ◽

State Of The Art ◽

Tracking Performance ◽

Scene Analysis ◽

Statistical Characteristics ◽

Robust Tracking ◽

Person Following ◽

Robotic Applications ◽

Real Target

While the robotics techniques have not developed to full automation, robot following is common and crucial in robotic applications to reduce the need for dedicated teleoperation. To achieve this task, the target must first be robustly and consistently perceived. In this paper, a robust visual tracking approach is proposed. The approach adopts a scene analysis module (SAM) to identify the real target and similar distractors, leveraging statistical characteristics of cross-correlation responses. Positive templates are collected based on the tracking confidence constructed by the SAM, and negative templates are gathered by the recognized distractors. Based on the collected templates, response fusion is performed. As a result, the responses of the target are enhanced and the false responses are suppressed, leading to robust tracking results. The proposed approach is validated on an outdoor robot-person following dataset and a collection of public person tracking datasets. The results show that our approach achieved state-of-the-art tracking performance in terms of both the robustness and AUC score.

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

Sensors ◽

10.3390/s20072137 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2137 ◽

Cited By ~ 2

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.

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A Narrow Deep Learning Assisted Visual Tracking with Joint Features

Mathematical Problems in Engineering ◽

10.1155/2020/8659890 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Xiaoyan Qian ◽

Daihao Zhang

Keyword(s):

Visual Tracking ◽

Model Updating ◽

State Of The Art ◽

Gaussian Mixture ◽

Background Information ◽

Robust Tracking ◽

Data Set ◽

Tracking Method ◽

Benchmark Data ◽

Updating Procedure

A robust tracking method is proposed for complex visual sequences. Different from time-consuming offline training in current deep tracking, we design a simple two-layer online learning network which fuses local convolution features and global handcrafted features together to give the robust representation for visual tracking. The target state estimation is modeled by an adaptive Gaussian mixture. The motion information is used to direct the distribution of the candidate samples effectively. And meanwhile, an adaptive scale selection is addressed to avoid bringing extra background information. A corresponding object template model updating procedure is developed to account for possible occlusion and minor change. Our tracking method has a light structure and performs favorably against several state-of-the-art methods in tracking challenging scenarios on the recent tracking benchmark data set.

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Robust Visual Tracking with Discrimination Dictionary Learning

Advances in Multimedia ◽

10.1155/2018/7357284 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Yuanyun Wang ◽

Chengzhi Deng ◽

Jun Wang ◽

Wei Tian ◽

Shengqian Wang

Keyword(s):

Linear Combination ◽

Visual Tracking ◽

Dictionary Learning ◽

State Of The Art ◽

Tracking Algorithm ◽

Robust Tracking ◽

Tracking Algorithms ◽

Stable Representation ◽

Target Templates ◽

Appearance Models

It is a challenging issue to deal with kinds of appearance variations in visual tracking. Existing tracking algorithms build appearance models upon target templates. Those models are not robust to significant appearance variations due to factors such as illumination variations, partial occlusions, and scale variation. In this paper, we propose a robust tracking algorithm with a learnt dictionary to represent target candidates. With the learnt dictionary, a target candidate is represented with a linear combination of dictionary atoms. The discriminative information in learning samples is exploited. In the meantime, the learning processing of dictionaries can learn appearance variations. Based on the learnt dictionary, we can get a more stable representation for target candidates. Additionally, the observation likelihood is evaluated based on both the reconstruct error and dictionary coefficients with l1 constraint. Comprehensive experiments demonstrate the superiority of the proposed tracking algorithm to some state-of-the-art tracking algorithms.

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Visual Tracking Using Wang–Landau Reinforcement Sampler

Applied Sciences ◽

10.3390/app10217780 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7780

Author(s):

Dokyeong Kwon ◽

Junseok Kwon

Keyword(s):

Visual Tracking ◽

Deep Neural Network ◽

Learning Algorithm ◽

State Of The Art ◽

Tracking System ◽

Network Architectures ◽

Tracking Accuracy ◽

Algorithm Performance ◽

Q Learning ◽

Number Of Visits

In this study, we present a novel tracking system, in which the tracking accuracy can be considerably enhanced by state prediction. Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang–Landau sampling. In the proposed method, reinforcement learning is used to predict a target configuration for the subsequent frame, while Wang–Landau sampler balances the exploitation and exploration degrees of the prediction. Our method can adapt to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. Numerical results demonstrate that our method substantially outperforms other state-of-the-art visual trackers and runs in realtime because our method contains no complicated deep neural network architectures.

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Adaptive Context-Aware and Structural Correlation Filter for Visual Tracking

Applied Sciences ◽

10.3390/app9071338 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1338 ◽

Cited By ~ 1

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.

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30. Radial Velocities (Vitesses Radiales)

Transactions of the International Astronomical Union ◽

10.1017/s0251107x00007227 ◽

1988 ◽

Vol 20 (01) ◽

pp. 355-362 ◽

Cited By ~ 1

Author(s):

J. Andersen ◽

D. W. Latham ◽

A. Florsch ◽

E. Maurice ◽

M. Mayor ◽

...

Keyword(s):

Cross Correlation ◽

State Of The Art ◽

Present Report ◽

Radial Velocities ◽

Developmental Phase ◽

Preliminary Results ◽

Reduction Techniques ◽

Current State ◽

Correlation Techniques ◽

General Adoption

The present report on the activities of IAU Commission 30, covering the triennium June l, 1984 through June 1, 1987, will be somewhat different from its recent predecessors in both content and style. Over the preceding decade or so, the reports mainly emphasized the dramatic improvements in observing efficiency, achieved primarily through the general adoption of cross-correlation techniques, combined with modern detectors attached to either specialized spectrometers or to existing, more conventional instruments. A great surge of observational activity followed, directed towards a variety of astrophysical problems, some of which are of a more classical nature, but many of which are in entirely new classes of research. At the time of the previous reports, most of the major observational projects were still underway, even if some preliminary results were emerging. The proceedings of IAU Colloquium No. 88,Stellar Radial Velocities(L. Davis Press, 1985) contains a collection of papers on instrumentation and reduction techniques as well as on ongoing observing programs which remains a very useful source of references to this developmental phase as well as to the current state of the art.

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Bio-inspired artificial pheromone system for swarm robotics applications

Adaptive Behavior ◽

10.1177/1059712320918936 ◽

2020 ◽

pp. 105971232091893

Author(s):

Seongin Na ◽

Yiping Qiu ◽

Ali E Turgut ◽

Jiří Ulrich ◽

Tomáš Krajník ◽

...

Keyword(s):

Swarm Robotics ◽

State Of The Art ◽

Rfid Tags ◽

Robot Swarm ◽

Pheromonal Communication ◽

Pheromone System ◽

Spatio Temporal ◽

Effective Use ◽

Robotic Applications ◽

Artificial Pheromone

Pheromones are chemical substances released into the environment by an individual animal, which elicit stereotyped behaviours widely found across the animal kingdom. Inspired by the effective use of pheromones in social insects, pheromonal communication has been adopted to swarm robotics domain using diverse approaches such as alcohol, RFID tags and light. COSΦ is one of the light-based artificial pheromone systems which can emulate realistic pheromones and environment properties through the system. This article provides a significant improvement to the state-of-the-art by proposing a novel artificial pheromone system that simulates pheromones with environmental effects by adopting a model of spatio-temporal development of pheromone derived from a flow of fluid in nature. Using the proposed system, we investigated the collective behaviour of a robot swarm in a bio-inspired aggregation scenario, where robots aggregated on a circular pheromone cue with different environmental factors, that is, diffusion and pheromone shift. The results demonstrated the feasibility of the proposed pheromone system for use in swarm robotic applications.

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

The Scientific World JOURNAL ◽

10.1155/2014/402185 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 3

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.

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Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1037.373 ◽

2014 ◽

Vol 1037 ◽

pp. 373-377 ◽

Cited By ~ 1

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.

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