High Performance Visual Tracking with Siamese Region Proposal Network

Li, Bo; Yan, Junjie; Wu, Wei; Zhu, Zheng; Hu, Xiaolin

doi:10.1109/cvpr.2018.00935

Cited by 2,311 publications

(2,006 citation statements)

References 21 publications

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“…The small90 benchmark: In Fig. 7, we further show the precision and success plots of 30 state-of-the-art trackers including SiamRPN [29] [30], LDES [31], SAT [32], TLD [3], LCT [33], OCT [22], CSK [34], CT [35], STC [36], KCF [37], ECO [38], MDNet [39], LCCF [40], SRDCF [41] and CPF [42], generated by the benchmark toolbox. While several baseline algorithms, e.g., LDES, DaSiamRPN, ECO, have shown promising potential in tracking small objects, our AST still helps achieve the precision rates of 84.9% (LDES AST), 83.1% (DaSiamRPN AST), 83.2% (ECO AST) which improve its counterpart base trackers by 1.6%, 0.9%, 1.7% respectively.…”

Section: Aggregation Signature On Trackingmentioning

confidence: 99%

Aggregation Signature for Small Object Tracking

Liu

Ding

Yang

et al. 2020

IEEE Trans. on Image Process.

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Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://github.com/bczhangbczhang/.

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Section: Aggregation Signature On Trackingmentioning

confidence: 99%

Aggregation Signature for Small Object Tracking

Liu

Ding

Yang

et al. 2020

IEEE Trans. on Image Process.

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“…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.…”

Section: Related Workmentioning

confidence: 99%

“…SiamFC-tri [38] incorporates a novel triplet loss into the Siamese network to extract expressive deep features. SiameseRPN [39] proposes an offline trained Siamese Region Proposal Network (RPN). DaSiameseRPN [42] improves SiameseRPN by introducing a distractor-aware module.…”

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 =

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High Performance Visual Tracking with Siamese Region Proposal Network

Cited by 2,311 publications

References 21 publications

Aggregation Signature for Small Object Tracking

Aggregation Signature for Small 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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