Object Tracking Benchmark

By Yi Wu, Jongwoo Lim, Ming-Hsuan Yang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

3,212 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c4c45661501c16064eead6e5d37dcb80d41c7a78

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Deeper and Wider Siamese Networks for Real-Time Visual Tracking (2019, methodology): “The evaluation on OTB-2013 and OTB-2015 follows the standard protocols proposed in [37, 38].”
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (2017, methodology): “To verify the effectiveness of the components of ADNet, we conducted four variants of ADNet and evaluated them using OTB-100.”
Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models (2017, background): “Recently, the benchmark has been extended (Wu, Lim, and Yang 2015).”
Real-Time MDNet (2018, methodology): “5 illustrates the precision and success plots on TempleColor dataset [30], which is containing 128 color videos while most of sequences are overlapped with OTB2015 dataset [28].”
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking (2018, methodology): “Recently, considering the joint circular structure of sliding candidates, Discriminative Correlation Filter (DCF) based tracking methods [7], [8] have achieved outstanding performance in many challenging benchmarks and competitions [5], [9], [10].”
Learning Background-Aware Correlation Filters for Visual Tracking (2017, methodology): “All sequences in OTB100 are manually annotated by 11 different visual attributes such as occlusion, deformation and motion blur.”
Is First Person Vision Challenging for Object Tracking? (2020, positive): “Nowadays, the state-of-the-art tracking solutions achieve excellent results on a large variety of tracking domains [85, 60, 31, 46].”
Ocean: Object-aware Anchor-free Tracking (2020, methodology): “The standardized OTB-100 [44] benchmark consists of 100 videos.”
AI Benchmark: Running Deep Neural Networks on Android Smartphones (2018, background): “One notable group of such tasks is concerned with computer vision problems like image classification [1, 2, 3], image enhancement [4, 5, 6] and super-resolution [7, 8, 9], optical character recognition [10], object tracking [11, 12], visual scene understanding [13, 14], face detection and recognitio”
Energy-Based Models for Deep Probabilistic Regression (2020, background): “Hyperparameters are set on the OTB [56] and NFS [25] datasets, containing 100 videos each.”
Know Your Surroundings: Exploiting Scene Information for Object Tracking (2020, methodology): “We evaluate our proposed tracking architecture on five tracking benchmarks: VOT2018 [29], GOT-10k [23], TrackingNet [36], OTB-100 [45], and NFS [14].”
AiATrack: Attention in Attention for Transformer Visual Tracking (2022, methodology): “We compare our tracker with several state-of-the-art trackers on three prevailing large-scale benchmarks (LaSOT [17], TrackingNet and [45] and GOT-10k [25]) and three commonly used small-scale datasets (NfS30 [30], OTB100 [55] and UAV123 [44]).”
The Visual Object Tracking VOT2015 Challenge Results (2015, methodology): “Several works that focus on performance evaluation in short-term visual object tracking [38, 36, 34, 64, 65, 76, 61, 77, 42] have been published over the last three years.”
Matching Pixels Using Co-occurrence Statistics (2018, background): “[5] from 35 annotated color video sequences of the OTB dataset [31].”
Siamese Box Adaptive Network for Visual Tracking (2020, background): “Prior to SiamRPN++, due to the limited representation capabilities of shallow networks, the Siamese network based [52] trackers achieves suboptimal performance on the OTB100.”
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking (2016, methodology): “To evaluate our approach, we perform comprehensive experiments on three benchmark datasets: OTB-2015 [27], VOT-2015 [17] and Temple-Color [20].”