Learning Temporary Block-Based Bidirectional Incongruity-Aware Correlation Filters for Efficient UAV Object Tracking

Lin, Fuling; Fu, Changhong; He, Yujie; Guo, Fuyu; Tang, Qian

doi:10.1109/tcsvt.2020.3023440

Cited by 28 publications

(17 citation statements)

References 59 publications

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“…The target in these sequences undergoes challenging attributes such as rotation, scale variation, occlusion, motion blur, and fast motion. The compared trackers include AutoTrack [23], ARCF [23], CFWCR [20], ECO [14], LADCF-HC [45], STRCF [25] and TB-BiCF [30]. It shows that the proposed STAR (in red box) achieves much better tracking precision compared with other SOTA trackers.…”

Section: Qualitative Evaluationsmentioning

confidence: 99%

“…The TC128 benchmark [29] contains 128 challenging color sequences. We compare the proposed STAR tracker with some SOTA DCF-based trackers, e.g., MCCT [41], LADCF-HC [45], MCCT-HC [41], STRCF [25], ECO-HC [14], CFWCR [20], MCPF [46], UDT+ [42], ARCF [23], UDT [42], AutoTrack [28], STRAPLE_CA [34], ARCF-H [23], DR2Track [17], BACF [18], TB-BiCF [30], RSST [47] and fDSST [15]. The success and precision plots of the evaluated trackers are depicted in Fig.…”

Section: Evaluation On Tc128mentioning

confidence: 99%

“…The OTB2013 and OTB2015 are two popular tracking benchmarks, which consist of 50 and 100 video sequences, respectively. We compare the proposed STAR with several representative trackers, including ECO [14], DeepSTRCF [25], STRCF [25], LADCF-HC [45], CFWCR [20], MCCT-HC [41], BACF [18], ECO-HC [14], UDT [42], ARCF [23], ARCF-H [23], UDT+ [42], AutoTrack [28], STAPLE_CA [34], TB-BiCF [30], fDSST [15], RSST [47] and DR2Track [17]. The overall comparison results on OTB2013 [43] and OTB2015 [44] are presented in Fig.…”

Section: Evaluation On Otb2013 and Otb2015mentioning

confidence: 99%

“…Compared with the generic object tracking, UAV-based tracking is to locate a certain target from a low-altitude aerial perspective, which poses new challenges, e.g., rapid changes in scale and perspective, limited pixels in the target region, and multiple similar disruptors [48]. The compared trackers include CFWCR [20], DeepSTRCF [25], UDT+ [42], ECO-HC [14], LADCF-HC [45], STRCF [25], UDT [42], AutoTrack [28], TB-BiCF [30], ARCF [23], RSST [47], DR2Track [17], BACF [18], MCCT-H [41], ARCF-H [23], STAPLE_CA [34] and fDSST [15]. The comparative results are presented in Fig.…”

Section: Evaluation On Uav123mentioning

confidence: 99%

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Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

Kim

Wang

et al. 2021

Complex Intell. Syst.

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The discriminative correlation filter (DCF)-based tracking methods have achieved remarkable performance in visual tracking. However, the existing DCF paradigm still suffers from dilemmas such as boundary effect, filter degradation, and aberrance. To address these problems, we propose a spatio-temporal joint aberrance suppressed regularization (STAR) correlation filter tracker under a unified framework of response map. Specifically, a dynamic spatio-temporal regularizer is introduced into the DCF to alleviate the boundary effect and filter degradation, simultaneously. Meanwhile, an aberrance suppressed regularizer is exploited to reduce the interference of background clutter. The proposed STAR model is effectively optimized using the alternating direction method of multipliers (ADMM). Finally, comprehensive experiments on TC128, OTB2013, OTB2015 and UAV123 benchmarks demonstrate that the STAR tracker achieves compelling performance compared with the state-of-the-art (SOTA) trackers.

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Section: Qualitative Evaluationsmentioning

confidence: 99%

Section: Evaluation On Tc128mentioning

confidence: 99%

Section: Evaluation On Otb2013 and Otb2015mentioning

confidence: 99%

Section: Evaluation On Uav123mentioning

confidence: 99%

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Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

Kim

Wang

et al. 2021

Complex Intell. Syst.

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show abstract

“…BICF has the ability to quickly learn to adjust in looks, as well as suppression of inconsistent errors. Then, TB_BICF is proposed as a variant of BIFC in [ 46 ]. The TB_BICF proposed a novel bidirectional incongruity-aware correlation filter using the nature of tracking reversibility.…”

Section: Background and Related Workmentioning

confidence: 99%

A practical evaluation of correlation filter-based object trackers with new features

et al. 2022

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Visual object tracking is a critical problem in the field of computer vision. The visual object tracker methods can be divided into Correlation Filters (CF) and non-correlation filters trackers. The main advantage of CF-based trackers is that they have an accepted real-time tracking response. In this article, we will focus on CF-based trackers, due to their key role in online applications such as an Unmanned Aerial Vehicle (UAV), through two contributions. In the first contribution, we proposed a set of new video sequences to address two uncovered issues of the existing standard datasets. The first issue is to create two video sequence that is difficult to be tracked by a human being for the movement of the Amoeba under the microscope; these two proposed video sequences include a new feature that combined background clutter and occlusion features in a unique way; we called it hard-to-follow-by-human. The second issue is to increase the difficulty of the existing sequences by increasing the displacement of the tracked object. Then, we proposed a thorough, practical evaluation of eight CF-base trackers, with the top performance, on the existing sequence features such as out-of-view, background clutters, and fast motion. The evaluation utilized the well-known OTB-2013 dataset as well as the proposed video sequences. The overall assessment of the eight trackers on the standard evaluation metrics, e.g., precision and success rates, revealed that the Large Displacement Estimation of Similarity transformation (LDES) tracker is the best CF-based tracker among the trackers of comparison. On the contrary, with a deeper analysis, the results of the proposed video sequences show an average performance of the LDES tracker among the other trackers. The eight trackers failed to capture the moving objects in every frame of the proposed Amoeba movement video sequences while the same trackers managed to capture the object in almost every frame of the sequences of the standard dataset. These results outline the need to improve the CF-based object trackers to be able to process sequences with the proposed feature (i.e., hard-to-follow-by-human).

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Color-saliency-aware correlation filters with approximate affine transform for visual tracking

Liu²,

Yan³

et al. 2022

Vis Comput

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Learning Temporary Block-Based Bidirectional Incongruity-Aware Correlation Filters for Efficient UAV Object Tracking

Cited by 28 publications

References 59 publications

Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

A practical evaluation of correlation filter-based object trackers with new features

Color-saliency-aware correlation filters with approximate affine transform for visual tracking

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