An Occlusion-Aware RGB-D Visual Object Tracking Method Based on Siamese Network

Zhang, Wenli; Yang, Kun; Xin, Yitao; Ma, Rui

doi:10.1109/icsp48669.2020.9320907

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Wu et al [18] used a hard example discrimination method to estimate occlusion occurrence. Wenil et al [19] used depth information to predict occlusion and precise object location. Fan et al [20] proposed predefined masks at different locations and took these masks as the conditions to guide occlusion-aware feature learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These works reflect the importance of occlusion information in tracking a visual object. However, the existing methods for addressing occlusion [17] [18] [21] [19] [20] are unsupervised, and there was no direct metric to quantify their performance on occlusion identification ability as well. This work focuses on proposing supervised occlusion aware networks and highlights their ability to identify occlusion for effective tracking.…”

Section: Literature Reviewmentioning

confidence: 99%

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GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

Dasari,

Gorthi

2023

Preprint

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Occlusionis a frequent phenomenon that hinders the task of visual object tracking. Since occlusion can be from any object and in any shape, data augmentation techniques will not greatly help identify or mitigate the tracker loss. Some of the existing works deal with occlusion only in an unsupervised manner. This paper proposes a generic deep learning framework for identifying occlusion in a given frame by formulating it as a supervised classification task for the first time. The proposed architecture introduces an ''occlusion classification" branch into supervised trackers. This branch helps in the effective learning of features and also provides occlusion status for each frame. A metric is proposed to measure the performance of trackers under occlusion at frame level. The efficacy of the proposed framework is demonstrated on two supervised tracking paradigms: One is from the most commonly used Siamese region proposal class of trackers, and another from the emerging Transformer-based trackers. This framework is tested on six diverse datasets (GOT-10k, LaSOT, OTB2015, TrackingNet, UAV123, and VOT2018), and it achieved significant improvements in performance over the corresponding baselines while performing on par with the state-of-the-art trackers. The contributions in this work are more generic, as any supervised tracker can easily adopt them.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

Dasari,

Gorthi

2023

Preprint

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“…Some representative models are briefly introduced here. In addition to the Siamese tracking network, SiamOC [64] provides two modules to consider both depth histogram characteristics and movement smoothness, simultaneously. The underlying assumption is that different objects have different depth histogram characteristics.…”

Section: Heuristic/deep Modelsmentioning

confidence: 99%

RGBD Object Tracking: An In-depth Review

Yang¹,

Li²,

Yan³

et al. 2022

Preprint

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RGBD object tracking is gaining momentum in computer vision research thanks to the development of depth sensors. Although numerous RGBD trackers have been proposed with promising performance, an in-depth review for comprehensive understanding of this area is lacking. In this paper, we firstly review RGBD object trackers from different perspectives, including RGBD fusion, depth usage, and tracking framework. Then, we summarize the existing datasets and the evaluation metrics. We benchmark a representative set of RGBD trackers, and give detailed analyses based on their performances. Particularly, we are the first to provide depth quality evaluation and analysis of tracking results in depth-friendly scenarios in RGBD tracking. For long-term settings in most RGBD tracking videos, we give an analysis of trackers' performance on handling target disappearance. To enable better understanding of RGBD trackers, we propose robustness evaluation against input perturbations. Finally, we summarize the challenges and provide open directions for this community. All resources are publicly available at https://github.com/memoryunreal/RGBD-tracking-review.

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GOA-net: generic occlusion aware networks for visual tracking

Dasari,

Gorthi

2024

Machine Vision and Applications

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An Occlusion-Aware RGB-D Visual Object Tracking Method Based on Siamese Network

Cited by 4 publications

References 28 publications

GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

RGBD Object Tracking: An In-depth Review

GOA-net: generic occlusion aware networks for visual tracking

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