Long-Term Visual Object Tracking Benchmark

Moudgil, Abhinav; Gandhi, Vineet

doi:10.1007/978-3-030-20890-5_40

Cited by 50 publications

(63 citation statements)

References 46 publications

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“…Two long-term tracking datasets have been proposed in concurrent work [21,18], both of which include sequences with labelled target absences. However, to our knowledge, neither provides a test set with secret ground-truth.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Tracking in the Wild: A Benchmark

Valmadre

Bertinetto

Henriques

et al. 2018

Lecture Notes in Computer Science

142

164

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We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seconds in length and in which the target is always visible. Consequently, most researchers have designed methods tailored to this "short-term" scenario, which is poorly representative of practitioners' needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 hours of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used "in the wild". The project website is oxuva.net.

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Section: Related Workmentioning

confidence: 99%

Long-Term Tracking in the Wild: A Benchmark

Valmadre

Bertinetto

Henriques

et al. 2018

Lecture Notes in Computer Science

142

164

View full text Add to dashboard Cite

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“…The popularity has been propelled by significant research challenges tracking offers as well as the industrial potential of tracking-based applications. Several initiatives have been established to promote tracking, such as PETS [95], CAVIAR 61 , i-LIDS 62 , ETISEO 63 , CDC [25], CVBASE 64 , FERET [67], LTDT 65 , MOTC [44,76] and Videonet 66 , and since 2013 short-term single target visual object tracking has been receiving a strong push toward performance evaluation standardisation from the VOT 60 initiative. The primary goal of VOT is establishing datasets, evaluation measures and toolkits as well as creating a platform for discussing evaluation-related issues through organization of tracking challenges.…”

Section: Introductionmentioning

confidence: 99%

The Sixth Visual Object Tracking VOT2018 Challenge Results

Kristan¹,

Leonardis²,

Matas³

et al. 2019

Lecture Notes in Computer Science

452

674

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The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .

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“…The field of visual object tracking has progressed significantly in recent years. The area encompasses a wide range of problems, including single object modelfree short-term tracking where a single target is localized in a video sequence given a single training example [39,17,19,18], long-term tracking methods requiring redetection and learning [15,26,25,34], multitarget multi-camera tracking [27], multi-view methods [20] and methods targeting specific objects such as cars [3], humans [24], or animals [10]. Many variants of the problems have been considered -static or dynamic cameras or environments, RGBD input, use of inertial measurement units to name a few.…”

Section: Introductionmentioning

confidence: 99%

Intra-Frame Object Tracking by Deblatting

Kotera¹,

Rozumnyi²,

Šroubek³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Objects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects elapse non-negligible distance during exposure time of a single frame and therefore their position in the frame is not well defined. They appear as semitransparent streaks due to the motion blur and cannot be reliably tracked by standard trackers.We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object. Blur is estimated by solving two intertwined inverse problems, blind deblurring and image matting, which we call deblatting. The trajectory is then estimated by fitting a piecewise quadratic curve, which models physically justifiable trajectories. As a result, tracked objects are precisely localized with higher temporal resolution than by conventional trackers.The proposed TbD tracker was evaluated on a newly created dataset of videos with ground truth obtained by a high-speed camera using a novel Trajectory-IoU metric that generalizes the traditional Intersection over Union and measures the accuracy of the intra-frame trajectory. The proposed method outperforms baseline both in recall and trajectory accuracy.

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Long-Term Visual Object Tracking Benchmark

Cited by 50 publications

References 46 publications

Long-Term Tracking in the Wild: A Benchmark

Long-Term Tracking in the Wild: A Benchmark

The Sixth Visual Object Tracking VOT2018 Challenge Results

Intra-Frame Object Tracking by Deblatting

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