Visual Tracking via Spatially Aligned Correlation Filters Network

Zhang, Mengdan; Wang, Qiang; Gao, Jin; Peng, Peixi; Hu, Weiming; Maybank, Stephen J.

doi:10.1007/978-3-030-01219-9_29

Cited by 55 publications

(29 citation statements)

References 37 publications

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“…A popular method for visual object tracking is learning Discriminative Correlation Filters (DCF) to predict the location of the tracked object in a patch [65][66][67][68][69][70][71][72][73][74][75][76][77][78][79]. A basic correlation filter based tracking framework is shown in Figure 3.…”

Section: Cf-based Trackersmentioning

confidence: 99%

Benchmarking Deep Trackers on Aerial Videos

Taufique

Minnehan

Savakis

2020

Sensors

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In recent years, deep learning-based visual object trackers have achieved state-of-the-art performance on several visual object tracking benchmarks. However, most tracking benchmarks are focused on ground level videos, whereas aerial tracking presents a new set of challenges. In this paper, we compare ten trackers based on deep learning techniques on four aerial datasets. We choose top performing trackers utilizing different approaches, specifically tracking by detection, discriminative correlation filters, Siamese networks and reinforcement learning. In our experiments, we use a subset of OTB2015 dataset with aerial style videos; the UAV123 dataset without synthetic sequences; the UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our benchmark datasets. We compare the advantages and disadvantages of different trackers in different tracking situations encountered in aerial data. Our findings indicate that the trackers perform significantly worse in aerial datasets compared to standard ground level videos. We attribute this effect to smaller target size, camera motion, significant camera rotation with respect to the target, out of view movement, and clutter in the form of occlusions or similar looking distractors near tracked object.

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Section: Cf-based Trackersmentioning

confidence: 99%

Benchmarking Deep Trackers on Aerial Videos

Taufique

Minnehan

Savakis

2020

Sensors

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“…In essence, they are related to tracking-by-detection methods, since they can learn a discriminative regressor from foreground and background samples. Many attempts have been done to improve the original correlation filter model in terms of scale estimation [23], re-detection [24], kernelized correlation [59], complementary cues [60], deep feature integrations [11], [12], [61], spatial regularization [62]- [65], to name a few.…”

Section: Relate Workmentioning

confidence: 99%

Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

Huang

2019

IEEE Access

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Visual object tracking in unconstrained environments is a challenging task in computer vision. How to design an efficient discriminative feature representation is one challenging issue. To improve the adaptability of the tracker to large object appearance changes, the observation model needs to be updated online. However, a bad model update using inaccurate training samples can lead to model drift problem. Therefore, how to design an efficient online observation model and a model update strategy are two other challenging issues. This paper proposes the concatenation of histogram of oriented gradients variant (HOGv) and color histogram as the feature representation to balance discriminative power and efficiency. The single-hidden-layer feedforward neural network (SFNN) is used as an observation model, and the recursive orthogonal least squares (ROLS) algorithm is used to update the model online. A bidirectional tracking scheme is designed to alleviate the model drift problem during online tracking. The proposed bidirectional tracking scheme consists of three modules: the forward tracking module, the backward tracking module and the integration module. The forward tracking module first finds all the candidate regions, and then, the backward tracking module calculates the respective confidence of each candidate region according to historical information. Finally, the integration module integrates both of the first two modules' results to determine the final tracked object and the model update strategy for the current frame. Extensive evaluations of the existing tracking benchmarks have shown that the proposed tracking framework results in significant performance improvements compared with the base tracker, and it outperforms most of the state-of-the-art trackers. INDEX TERMS Visual object tracking, bidirectional tracking scheme, recursive orthogonal least squares, model update mechanism.

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“…This method obtained accurate object boundaries and improvements for lowering computation times. Zhang et al [29] introduced a spatial alignment module, which provides continuous feedback for transforming the target from the border to the center with a normalized aspect ratio. This method can handle undesired boundary effects.…”

Section: Related Workmentioning

confidence: 99%

A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion

et al. 2018

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Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and illumination changes. In this paper, we utilize the hierarchical features of convolutional neural networks (CNNs) and learn a spatial-temporal context correlation filter on convolutional layers. Then, the translation is estimated by fusing the response score of the filters on the three convolutional layers. In terms of scale estimation, we learn a discriminative correlation filter to estimate scale from the best confidence results. Furthermore, we proposed a re-detection activation discrimination method to improve the robustness of visual tracking in the case of tracking failure and an adaptive model update method to reduce tracking drift caused by noisy updates. We evaluate the proposed tracker with DCFs and deep features on OTB benchmark datasets. The tracking results demonstrated that the proposed algorithm is superior to several state-of-the-art DCF methods in terms of accuracy and robustness.

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Visual Tracking via Spatially Aligned Correlation Filters Network

Cited by 55 publications

References 37 publications

Benchmarking Deep Trackers on Aerial Videos

Benchmarking Deep Trackers on Aerial Videos

Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion

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