Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

Huang, Zhiyong; Yu, Yuanlong; Xu, Miaoxing

doi:10.1109/access.2019.2951056

Cited by 7 publications

(4 citation statements)

References 70 publications

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“…In a related study to design a target tracking model with effective online observation and model updating capabilities, Huang et al [32] proposed representing the target features with the combination of direction gradient change and color histograms, while the single hidden layer feed-forward neural network and recursive orthogonal least-squares algorithm are used as target observation models. Aiming at the high time complexity of the Siamese trackers when used to estimate the scale and angle of the tracking target, Lee [33] proposed a single shot Siamese network that could estimate the size and angle of the target with a single search area.…”

Section: B Non-correlation Filter Trackermentioning

confidence: 99%

A Multi-Information Fusion Correlation Filters Tracker

et al. 2020

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In recent years, trackers based on correlation filters have attracted more and more attention due to the impressive tracking accuracy and real-time performance. However, in real scenarios, the tracking results are often been interfered with by the occlusion, illumination variation, appearance variation and background clutter. In order to find a tracker with better tracking performances, this paper proposed a multi-information fusion correlation filter tracker, which uses channel and spatial reliabilities and time regularization information on samples for filter training, and which not only extends the target search areas but also has a stronger ability to track the targets with significant appearance variations. Thus, results from extensive experiments conducted on OTB100, VOT2016, TC128, and UAV123 data sets show that our tracker with only directional gradient histogram (HOG) and color name (CN) features, performs favorably against the state-of-the-art trackers in terms of tracking precision, tracking success rate, tracking accuracy, and A-R rank. INDEX TERMS Object tracking, correlation filter, channel reliability, spatial reliability, time regularization.

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Section: B Non-correlation Filter Trackermentioning

confidence: 99%

A Multi-Information Fusion Correlation Filters Tracker

et al. 2020

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“…Therefore, many scholars introduce the recurrent neural network into object tracking, such as RTT [47], ROLO [48], SANet [49] and so on. Huang et al [50] proposed a Bidirectional Tracking for tracking based on recursive orthogonal least squares to update model strategy for model drift problem. However, the original RNN [51] has encountered vanished gradient problem when the image sequence is too long, which may hinder information learning spanning over a long sequence.…”

Section: ) Deep Learning Trackersmentioning

confidence: 99%

ADT: Object Tracking Algorithm Based on Adaptive Detection

Ming

Zhang

2020

IEEE Access

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Object tracking is one of the most fundamental and important fields in computer vision with a wide range of applications. Although great progress has been made in object tracking combined with detection, there is still enormous challenges in real-time applications and for the computer cannot effectively capture the temporal correlations of targets and background clutter. In order to improve the performance of tracking algorithms under complex unconstrained conditions, we propose a novel tracking framework based on adaptive detection, called adaptive detection tracking (ADT). First, we exploit the temporal correlation of the recurrent neural network to predict the target's motion direction and efficiently update the region of interest (RoI) in the narrow range of the next frame. Then, the algorithm utilizes the correlation filter to initialize the defined region of interest based on the threshold. If the Interaction of Union (IoU) of the predicted bounding box and the groundtruth bounding box is greater than the set threshold, the predicted bounding box will be directly output as the tracking results, whereas the detection is adaptively carried out in the determined RoI. Finally, the predicted bounding box refines the direction model as the input of the next frame to complete the whole tracking flow. Our proposed adaptive detection tracking mechanism can efficiently realize non-frame-by-frame adaptive detection with excellent tracking accuracy and is more robust in the unconstrained scenes, especially for occlusion. Comprehensive experiments demonstrate that our approach consistently achieves state-of-the-art results and runs in real-time on six large tracking benchmarks, including OTB100, VOT2016, VOT2017, TC128, UAV123 and LaSOT datasets. INDEX TERMS Recurrent neural network, adaptive detection, object tracking, correlation filtering, model compression. YUE MING received the B.S. degree in communication engineering, the M.Sc. degree in humancomputer interaction engineering, and the Ph.D. degree in signal and information processing from

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“…Machine learning and deep learning techniques have been the crucial tools when it comes to the feature extracting and event estimating for developing applications in the electronics industries [1][2][3][4][5][6][7][8]. Some techniques have been implemented in the embedded systems and applied to industry 4.0 applications, industrial electronics applications, consumer electronics applications, and other electronics applications.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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This editorial introduces the Special Issue, entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries”, of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) “A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence” by Panapakidis et al.; (2) “Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting” by Wan et al.; (3) “Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors” by Xu et al.; (4) “An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform” by Zhang et al. Three papers on UAV and object tracking applications are as follows: (1) “Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints” by Zhou et al.; (2) “OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance” by Zhang et al.; (3) “Model Update Strategies about Object Tracking: A State of the Art Review” by Wang et al. Five papers on measurement and denoising techniques are as follows: (1) “Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation” by Wang et al.; (2) “Characterization and Correction of the Geometric Errors Using a Confocal Microscope for Extended Topography Measurement, Part II: Experimental Study and Uncertainty Evaluation” by Wang et al.; (3) “Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction” by Lin et al.; (4) “Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets” by Chang et al.; (5) “High-Resolution Image Inpainting Based on Multi-Scale Neural Network” by Sun et al. Two papers on recommendation systems and education systems are as follows: (1) “Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing” by Sulikowski et al. and (2) “Generative Adversarial Network Based Neural Audio Caption Model for Oral Evaluation” by Zhang et al.

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Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

Cited by 7 publications

References 70 publications

A Multi-Information Fusion Correlation Filters Tracker

A Multi-Information Fusion Correlation Filters Tracker

ADT: Object Tracking Algorithm Based on Adaptive Detection

Deep Learning Applications with Practical Measured Results in Electronics Industries

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