Learning Dynamic Siamese Network for Visual Object Tracking

Guo, Qing; Feng, Wei; Zhou, Chao; Huang, Rui; Wan, Liang; Wang, Song

doi:10.1109/iccv.2017.196

Cited by 771 publications

(478 citation statements)

References 24 publications

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“…Template Combination. Algorithms [16,45] based on template combination aim to effectively combine the target features from previous frames. Guo et al [16] propose a fast transformation learning model to enable effective online learning from previous frames.…”

Section: Model Updating In Trackingmentioning

confidence: 99%

“…Whereas the success plot reports the percentages of frames where the overlap between the predicted and the ground truth bounding boxes is higher than a series of given ratios. We compare our algorithm with twelve state-of-theart trackers including nine real-time deep trackers (ACT [5], StructSiam [44], SiamRPN [22], ECO-HC [7], PTAV [13], CFNet [35], Dsiam [16], LCT [27], SiameFC [3]) and three traditional trackers (Staple [2], DSST [8], KCF [18]). Figure 6 illustrates the precision and success plots of all compared trackers over OTB-2015, which shows the proposed tracker achieves very good performance (merely a slightly lower than ECO-HC in success).…”

Section: Evaluation On the Otb-2015 Datasetmentioning

confidence: 99%

“…We also adopt both success and precision plots to evaluate different trackers (the same evaluation protocol as OTB-2015). We compare our algorithm with eleven trackers, including ACT [5], PTAV [13], Dsiam [16], SiameFC [3], HCFT [26], FCNT [36], STCT [37], BACF [15], SRDCF [9], KCF [18] and MEEM [41]. Figure 7 shows that our tracker achieves the best results in terms of both precision and success criterion.…”

Section: Evaluation On the Tc-128 Datasetmentioning

confidence: 99%

See 2 more Smart Citations

GradNet: Gradient-Guided Network for Visual Object Tracking

Chen

Ouyang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

281

122

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The fully-convolutional siamese network based on template matching has shown great potentials in visual tracking. During testing, the template is fixed with the initial target feature and the performance totally relies on the general matching ability of the siamese network. However, this manner cannot capture the temporal variations of targets or background clutter. In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations. To be specific, the algorithm can utilize the information from the gradient to update the template in the current frame. In addition, a template generalization training method is proposed to better use gradient information and avoid overfitting. To our knowledge, this work is the first attempt to exploit the information in the gradient for template update in siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than other state-of-the-art trackers. The source codes are available at

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Section: Model Updating In Trackingmentioning

confidence: 99%

Section: Evaluation On the Otb-2015 Datasetmentioning

confidence: 99%

Section: Evaluation On the Tc-128 Datasetmentioning

confidence: 99%

See 1 more Smart Citation

GradNet: Gradient-Guided Network for Visual Object Tracking

Chen

Ouyang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

281

122

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show abstract

“…Recently, several Siamese network based trackers [36,37,38,39,40,41] have been proposed to address the above problems, which can improve the tracking accuracy while preserving real-time speeds. For example, DSiam [36] proposes a dynamic Siamese network with transformation learning and EAST [37] learns a decision-making strategy in a reinforcement learning framework for adaptive tracking. SiamFC-tri [38] incorporates a novel triplet loss into the Siamese network to extract expressive deep features.…”

Section: Related Workmentioning

confidence: 99%

Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation

Yan

Chen

et al. 2020

Neurocomputing

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In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-based tracking methods exhibit excellent performance while their speeds are heavily limited by the expensive computation for massive proposal feature extraction. In contrast, matching-based tracking methods (such as Siamese networks) possess remarkable speed superiority.However, the absence of online updating renders these methods unadaptable to significant object appearance variations. In this paper, we propose a novel real-time visual tracking method, which adopts an object-adaptive LSTM network to effectively capture the video sequential dependencies and adaptively learn the object appearance variations. For high computational efficiency, we also present a fast proposal selection strategy, which utilizes the matching-based tracking method to pre-estimate dense proposals and selects high-quality ones to feed to the LSTM network for classification. This strategy efficiently filters out some irrelevant proposals and avoids the redundant computation for feature extraction, which enables our method to operate faster than conventional classification-based tracking methods. In addition, to handle the problems of sample inadequacy and class imbalance during online tracking, we adopt a data augmentation technique based on the Generative Adversarial Network (GAN) to facilitate the training of the LSTM network. Extensive experiments on four visual tracking benchmarks demonstrate the state-of-the-art performance of our method in terms of both tracking accuracy and speed, which exhibits great potentials of recurrent structures for visual tracking.

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“…the previous methodologies, however they are very computationally expensive and can run at just 1 and 6 FPS respectively. Currently, the approach based on the Siamese framework is getting significant attention for their well-balanced tracking accuracy and efficiency [2,11,31,30,54,53]. These trackers formulate the visual tracking as a cross-correlation problem and are leveraging effectively from end-to-end learning of DNNs.…”

Section: Visual Trackingmentioning

confidence: 99%

Visual Tracking by Means of Deep Reinforcement Learning and an Expert Demonstrator

Dunnhofer

Martinel

Foresti

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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In the last decade many different algorithms have been proposed to track a generic object in videos. Their execution on recent large-scale video datasets can produce a great amount of various tracking behaviours. New trends in Reinforcement Learning showed that demonstrations of an expert agent can be efficiently used to speed-up the process of policy learning. Taking inspiration from such works and from the recent applications of Reinforcement Learning to visual tracking, we propose two novel trackers, A3CT, which exploits demonstrations of a state-of-the-art tracker to learn an effective tracking policy, and A3CTD, that takes advantage of the same expert tracker to correct its behaviour during tracking. Through an extensive experimental validation on the GOT-10k, OTB-100, LaSOT, UAV123 and VOT benchmarks, we show that the proposed trackers achieve state-of-the-art performance while running in real-time. arXiv:1909.08487v1 [cs.CV] 18 Sep 2019 =

show abstract

Learning Dynamic Siamese Network for Visual Object Tracking

Cited by 771 publications

References 24 publications

GradNet: Gradient-Guided Network for Visual Object Tracking

GradNet: Gradient-Guided Network for Visual Object Tracking

Object-adaptive LSTM network for real-time visual tracking with adversarial data augmentation

Visual Tracking by Means of Deep Reinforcement Learning and an Expert Demonstrator

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