Efficient Visual Tracking With Stacked Channel-Spatial Attention Learning

Rahman, Md. Maklachur; Fiaz, Mustansar; Jung, Soon Ki

doi:10.1109/access.2020.2997917

Cited by 23 publications

(13 citation statements)

References 74 publications

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“…We noticed that the proposed method weakly performs in the low-resolution sequences depicted in Figures 4 and 5 compared with other trackers. We also observed that our method has a deficiency on TC128 benchmark against the SCSAtt [69] tracker that introduced stacked channel-spatial attention based Siamese network for tracking. SCSAtt utilized the spatial feature map to locate the target besides focusing on the object's informative part.…”

Section: Discussionmentioning

confidence: 85%

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Siamese High-Level Feature Refine Network for Visual Object Tracking

et al. 2020

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Siamese network-based trackers are broadly applied to solve visual tracking problems due to its balanced performance in terms of speed and accuracy. Tracking desired objects in challenging scenarios is still one of the fundamental concerns during visual tracking. This research paper proposes a feature refined end-to-end tracking framework with real-time tracking speed and considerable performance. The feature refine network has been incorporated to enhance the target feature representation power, utilizing high-level semantic information. Besides, it allows the network to capture the salient information to locate the target and learns to represent the target feature in a more generalized way advancing the overall tracking performance, particularly in the challenging sequences. But, only the feature refine module is unable to handle such challenges because of its less discriminative ability. To overcome this difficulty, we employ an attention module inside the feature refine network that strengths the tracker discrimination ability between the target and background. Furthermore, we conduct extensive experiments to ensure the proposed tracker’s effectiveness using several popular tracking benchmarks, demonstrating that our proposed model achieves state-of-the-art performance over other trackers.

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Section: Discussionmentioning

confidence: 85%

“…Similar to the OTB benchmark evaluation, we adopt success and precision plots to compare with the state-of-the-art trackers. We compare our tracker with SCSAtt [69], MEEM [63], SRDCF [61], MUSTER [65], SAMF [64], KCF [68], DSST [66], truck [67], and CSK [70] on this benchmark.…”

Section: Experiments On Tc128 Benchmarkmentioning

confidence: 99%

Siamese High-Level Feature Refine Network for Visual Object Tracking

et al. 2020

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“…A Siamese network comprises of two parallel Convolutional Neural Networks (CNN) streams that are used to learn the similarity between input images in embedded space and to fuse them to produce an output [ 46 ]. Owing to their inherent characteristics such as accuracy and speed, Siamese networks are popular in the visual tracking community [ 10 , 15 , 16 , 17 , 47 ]. A SiameseFC [ 15 ] extracts input image features using an embedded CNN model and fuses them by using a correlation layer, to generate a response map.…”

Section: Related Workmentioning

confidence: 99%

Improving Object Tracking by Added Noise and Channel Attention

Fiaz

Mahmood

Baek

et al. 2020

Sensors

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CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.

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“…Reference [ 90 ] proposed spatial attention (SCSAtt), which ensured the model’s speed and increases its robustness. SCSAtt uses weight allocation to highlight the importance of the feature of the channel—namely, the channel attention module—and uses the spatial attention module to highlight the area with the most information on the feature diagram to determine the target location.…”

Section: Target Tracking Algorithm Based On a Deep Learning Networmentioning

confidence: 99%

Aerial Video Trackers Review

Jia

Lai

Qian

et al. 2020

Entropy

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Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.

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Efficient Visual Tracking With Stacked Channel-Spatial Attention Learning

Cited by 23 publications

References 74 publications

Siamese High-Level Feature Refine Network for Visual Object Tracking

Siamese High-Level Feature Refine Network for Visual Object Tracking

Improving Object Tracking by Added Noise and Channel Attention

Aerial Video Trackers Review

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