Adaptive Feature Selection Siamese Networks for Visual Tracking

Fiaz, Mustansar; Rahman, Md. Maklachur; Mahmood, Arif; Farooq, Sehar Shahzad; Baek, Ki Yeol; Jung, Soon Ki

doi:10.1007/978-981-15-4818-5_13

Cited by 6 publications

(6 citation statements)

References 38 publications

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“…This mechanism is widely used in several fields of computer vision, including image classification [16], object detection [27], segmentation [28], and person reidentification [29]. Similarly, visual tracking frameworks [10,11,[13][14][15] adopt attention mechanisms to highlight the target features. This technique enables the model to handle challenges in tracking.…”

Section: Tracking With Attention Networkmentioning

confidence: 99%

“…However, techniques relying on template matching using metric learning extract the target template and choose the most similar candidate patch at the current frame. Siamese-based trackers [9][10][11][12][13][14][15] follow the template-matching strategy, which uses cross-correlation to reduce computational overhead and solve the tracking problem effectively. Siamese-based tracker, SiamFC [9], gains immense popularity to the tracking community.…”

Section: Introductionmentioning

confidence: 99%

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Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

Rahman¹,

Jung²

2022

Artificial Intelligence

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Tracking with the siamese network has recently gained enormous popularity in visual object tracking by using the template-matching mechanism. However, using only the template-matching process is susceptible to robust target tracking because of its inability to learn better discrimination between target and background. Several attention-learning are introduced to the underlying siamese network to enhance the target feature representation, which helps to improve the discrimination ability of the tracking framework. The attention mechanism is beneficial for focusing on the particular target feature by utilizing relevant weight gain. This chapter presents an in-depth overview and analysis of attention learning-based siamese trackers. We also perform extensive experiments to compare state-of-the-art methods. Furthermore, we also summarize our study by highlighting the key findings to provide insights into future visual object tracking developments.

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Section: Tracking With Attention Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

Rahman¹,

Jung²

2022

Artificial Intelligence

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“…CSRDCF [ 48 ] constructs a unique spatial reliability map to impose constraints on correlation filters within a correlation tracking framework. AFS-Siam [ 49 ] selects the discriminative kernels from different convolutional layers. Choi et al [ 24 ] proposed ACFN and used spatial attention to select a subset of correlation filters for visual object tracking.…”

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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“…It uses cross-correlation to match the template patch with the current frame of the video, which utilizes fewer operations and increases the network computational power. Due to its efficient computational ability, several trackers [11][12][13][14][15] are developed recently to solve the tracking problems using the Siamese network. However, they suffer a lack of robustness to handle challenging sequences, particularly background clutter, occlusion, and appearance changes because of offline training on the large dataset without considering the most discriminative features that reduce the performances.…”

Section: Introductionmentioning

confidence: 99%

Siamese High-Level Feature Refine Network for Visual Object Tracking

et al. 2020

Self Cite

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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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Adaptive Feature Selection Siamese Networks for Visual Tracking

Cited by 6 publications

References 38 publications

Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

Improving Object Tracking by Added Noise and Channel Attention

Siamese High-Level Feature Refine Network for Visual Object Tracking

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