The Sixth Visual Object Tracking VOT2018 Challenge Results

By Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman P. Pflugfelder, L. Č. Zajc, Tomás Vojír, Goutam Bhat, Alan Lukezic, A. Eldesokey and 143 more. ECCV Workshops, 2018

797 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/219e9a4527110baf1feb3df20db12064eeafdfb7

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Citations in the corpus

A review of computer vision–based structural health monitoring at local and global levels (2020, background): “The accuracy of deep learning–based object tracking has outperformed the traditional algorithms and seems to be going mainstream in object tracking area.(172) By building deep CNN and training the network with manually labeled dataset, the optimized neural network can achieve very good tracking perf”
Ocean: Object-aware Anchor-free Tracking (2020, methodology): “The effectiveness of the proposed framework is verified on five benchmarks: VOT-2018 [17], VOT-2019 [18], OTB-100 [44], GOT-10k [14] and LaSOT [8].”
STMTrack: Template-free Visual Tracking with Space-time Memory Networks (2021, methodology): “We evaluate our tracker on benchmarks: OTB-2015 [53], TrackingNet [36], GOT-10k [17], LaSOT [13], UAV123 [35], and VOT2018 [20].”
Probabilistic Regression for Visual Tracking (2020, methodology): “RCO UPDT DaSiam- MFT LADCF ATOM SiamRPN++ DiMP18 DiMP50 PrDiMP18 PrDiMP50 [25] [4] RPN [51] [25] [48] [9] [28] [3] [3]”
Learning Target Candidate Association to Keep Track of What Not to Track (2021, methodology): “VOT2018LT [35]: Next, we evaluate on the 2018 edition of the VOT long-term tracking challenge.”
Track Anything: Segment Anything Meets Videos (2023, background): “On the other hand, tracking or segmenting in videos faces challenges from scale variation, target deformation, motion blur, camera motion, similar objects, and so on [4, 8, 10, 7, 9, 6].”
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines (2019, background): “400 on VOT2018 (Kristan et al. 2018) benchmark while running at a speed over 100 FPS, which demonstrate its potential of being applied in real production cases.”
Siamese Box Adaptive Network for Visual Tracking (2020, background): “Comparison of attributes on VOT2018.”
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild (2018, background): “Furthermore, the evaluation of trackers is very time-consuming, thus it is advantageous to keep the test set relatively compact [3], [8].”
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking (2021, methodology): “We compare our proposed TrSiam and TrDiMP trackers with the recent state-of-the-art trackers on seven tracking benchmarks including TrackingNet [38], GOT-10k [23], LaSOT [13], VOT2018 [26], Need for Speed [24], UAV123 [36], and OTB-2015 [56].”
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking (2018, methodology): “Datasets: We evaluate the performance of our tracking method on five well-known benchmarks: OTB2013 [3], OTB50 [5], OTB100 [5], Temple-Colour [16], UAV123 [17] and VOT2018 [18].”
Know Your Surroundings: Exploiting Scene Information for Object Tracking (2020, methodology): “DRT RCO UPDT DaSiam- MFT LADCF ATOM SiamRPN++ DiMP-50 Ours [41] [29] [4] RPN [52] [29] [48] [8] [31] [3]”
Transforming Model Prediction for Tracking (2022, result): “In contrast to previous years where the sequences in the VOT short-term challenge were annotated with bounding boxes [29, 30] the sequences of the more recent challenges contain segmentation mask annotations [28, 31] of the target in each frame.”
Fast Online Object Tracking and Segmentation: A Unifying Approach (2018, methodology): “We adopt two widely used benchmarks for the evaluation of the object tracking task: VOT2016 [28] and VOT-2018 [29], both annotated with rotated bounding boxes.”