Vision Tracking: A Survey of the State-of-the-Art

Dutta, Anjan; Mondal, Atreyee; Dey, Nilanjan; Sen, Soumya; Moraru, Luminița; Hassanien, Aboul Ella

doi:10.1007/s42979-019-0059-z

Cited by 23 publications

(10 citation statements)

References 240 publications

(236 reference statements)

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“…Human attention can be extracted from the gesture and position of the human body, particularly from the eyes. Dutta et al [25] has provided a comprehensive review on vision tracking algorithms. To achieve a certain level of accuracy, machine learning methods required appropriate parameters such as loss function optimizers.…”

Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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Attention is the mental awareness of human on a particular object or a piece of information. The level of attention indicates how intense the focus is on an object or an instance. In this study, several types of human attention level have been observed. After introducing image segmentation and detection technique for facial features, eyeball movement and gaze estimation were measured. Eye movement were assessed using the video data, and a total of 10197 data instances were manually labelled for the attention level. Then Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based Deep learning (DL) architectures have been proposed for analysing the data. Next, the trained DL model has been implanted into a robotic system that is capable of detecting various features; ultimately leading to the calculation of visual attention for reading, browsing, and writing purposes. This system is capable of checking the attention level of the participants and also can detect if participants are present or not. Based on a certain level of visual focus of attention (VFOA), this system interacts with the person, generates awareness and establishes verbal or visual communication with that person. The proposed ML techniques have achieved almost 99.24% validation accuracy and 99.43% test accuracy. It is also shown in the comparative study that, since the dataset volumes are limited, ANN is more suitable for attention level calculation than RNN-LSTM. We hope that the implemented robotic structure manifests the real-world implication of the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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“…To choose these keyframes, first, the features in the first frame of the video are detected and tracked. The extracted features are tracked using the wellknown Kanade-Lucas-Tomasi tracker (KLT) [20], which is a feature-based tracking algorithm [21]. A keyframe is selected when the percentage of features that have been successfully tracked falls below a certain threshold.…”

Section: Visual Path Creationmentioning

confidence: 99%

Two Efficient Visual Methods for Segment Self-localization

2021

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Localization is an essential step in visual navigation algorithms in robotics. Some visual navigation algorithms define the environment through sequential images, which are called visual path. The interval between each consecutive image is called a segment. One crucial step in these kinds of navigation is to find the segment in which the robot is placed (segment selflocalization). Visual segment self-localization methods consist of two stages. In the first stage, a feature matching between the current image of the robot with all the images that form the visual path is done. Usually, in this stage, outliers removal methods such as RANSAC are used after matching to remove the outliers matched features. In the second stage, a segment is chosen depending on the results of the first one. Segment self-localization methods estimate a segment depending just on the percentage of the matched features. This leads to estimate the segment incorrectly in some cases. In this paper, another parameter also is considered to estimate the segment. The parameter is based on the perspective projection model. Moreover, instead of RANSAC which is a stochastic and time consuming method, a more straightforward and more effective method is proposed for outliers detection. The proposed methods are tested on Karlsruhe dataset, and acceptable results are obtained. Also, the methods are compared with three reviewed methods by Nguyen et al. (J Intell Robot Syst 84:217, 2016). Although the proposed methods use a more straightforward outlier method, they give more accurate results.

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“…This section will give insight of most closely related tracking schemes to the proposed method: i) correlation filter based tracking ii) tracking learning and detection also known as tracking by detection. Further detailed discussions are available in [2], [27], [28], [29], [30].…”

Section: Related Work and Problem Backgroundmentioning

confidence: 99%

AFAM-PEC: Adaptive Failure Avoidance Tracking Mechanism Using Prediction-Estimation Collaboration

et al. 2020

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During recent years correlation tracking is considered fast and effective by the virtue of circulant structure of the sampling data for learning phase of filter and Fourier domain calculation of correlation. During the occurrence of occlusion, motion blur and out of view movement of target, most of the correlation filter based trackers start to learn using erroneous samples and tracker starts drifting. Currently, adaptive correlation filter based tracking algorithms are being combined with redetection modules. This hybridization helps in redetection of the target in long term tracking. The redetection modules are mostly classifier, which classify the true object after tracking failure occurrence. The methods perform favorable during short term occlusion or partial occlusion. To further increase the tracking efficiency specifically during long term occlusion, while maintaining real time processing speed, this study proposes tracking failure avoidance method. We first propose, a strategy to detect the occlusion using two cues from the response map i.e., peak correlation score and peak to side lobe ratio. After successful detection of tracking failure, second strategy is proposed to save the target being getting more erroneous. Kalman filter based predictor continuously predicts the location during occlusion. Kalman filter passes this result to Support Vector Machine (SVM). When the target reappears in frame, support vector machine based classifier classifies the correct object using the predicted location of Kalman filter. This decreases the chance of tracking failure as Kalman filter continuously updates itself during occlusion and predicts the next location using its own previous prediction. Once the true object is detected by classifier after the clearance of occlusion, this result is forwarded to correlation filter tracker to resume its operation of tracking and updating its parameters. Together these two proposed schemes show significant improvement in tracking efficiency. Furthermore, this collaboration in redetection phase shows significant improvement in the tracking accuracy over videos containing six challenging aspects of visual object tracking as mentioned in the literature.

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Vision Tracking: A Survey of the State-of-the-Art

Cited by 23 publications

References 240 publications

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Two Efficient Visual Methods for Segment Self-localization

AFAM-PEC: Adaptive Failure Avoidance Tracking Mechanism Using Prediction-Estimation Collaboration

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