A Benchmark and Simulator for UAV Tracking

By Matthias Mueller, Neil G. Smith, Bernard Ghanem. European Conference on Computer Vision, 2016

1,861 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/27850781e39df9f750e05409b8072261124068e8

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking (2019, methodology): “More specifically, ARCF-HC has achieved an advancement of 8.1% (UAV123@10fps), 9.8% (DTB70) and 5.2% (UAVDT) respectively from BACF in AUC in occlusion cases.”
Energy-Based Models for Deep Probabilistic Regression (2020, methodology): “The results are shown in Table 3, and the success plot on UAV123 is shown in Figure 3b.”
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, methodology): “The following datasets are collected by aerial vehicles, including VIRAT Video Dataset [68] for event recognition, UAV123 [69] for UAV tracking, and a multipurpose dataset [70].”
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking (2018, methodology): “Summaries of existing tracking benchmarks with highquality dense (per frame) annotations, including OTB-2013 [52], OTB-2015 [53], TC-128 [35], NUS-PRO [28], UAV123 [39], UAV20L [39], VOT-2014 [26], VOT-2017 [27] and LaSOT.”
Vision Meets Drones: A Challenge (2018, background): “Some recent efforts [9], [10], [11] have been devoted to construct datasets with drone platform focusing on object detection or tracking.”
AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization (2020, methodology): “On UAV123@10fps, AutoTrack achieves a remarkable advantage of 5.8% in precision than the second best ECO-HC, proving its robustness against large motion.”
Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking (2022, methodology): “We compare our SimTrack with state-of-the-art trackers on five datasets, including LaSOT [10], TNL2K [41], TrackingNet [30], UAV123 [29] and GOT-10k [19].”
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines (2019, neutral): “Being a fundamental build block in various areas of computer vision, the task comes with a variety of applications such as UAV-based monitoring (Mueller, Smith, and Ghanem 2016) and surveillance system (Kokkeby et al. 2015).”
ECO: Efficient Convolution Operators for Tracking (2016, methodology): “UAV123 Dataset: Aerial tracking using unmanned aerial vehicles (UAVs) has received much attention recently, with many vision applications, including wild-life monitoring, search and rescue, navigation, and crowd surveillance.”
Graph Attention Tracking (2020, neutral): “We evaluate our SiamGAT on several challenge benchmarks, including GOT-10k [14], OTB-100 [31], UAV123 [21] and LaSOT [7].”
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving (2020, background): “[52] 2020 A OTB100 [53], UAV123 [54], NFS [55] and four others”
The Visual Object Tracking VOT2017 Challenge Results (2017, background): “Performance evaluation of short-term visual object trackers has received significant attention in the last five years [32, 33, 30, 31, 29, 68, 60, 77, 39, 40, 45, 41].”
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework (2022, methodology): “NFS, UAV123 and TNL2K.”
ATOM: Accurate Tracking by Overlap Maximization (2018, methodology): “We evaluate the proposed tracker ATOM on five benchmarks: Need for Speed (NFS) [9], UAV123 [24], TrackingNet [25], LaSOT [8], and VOT2018 [17].”
Deformable Siamese Attention Networks for Visual Object Tracking (2020, methodology): “UAV123 [29].”
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark (2020, methodology): “1 Summary of existing benchmarks with precise dense (per frame) annotations using log-log scale, containing OTB-13 [83], OTB-15 [84], TC-128 [57], NUS-PRO [47], UAV123 [65], UAV20L [65], CDTB [60], VOT-2017 [1], GOT10k [41] and LaSOT.”
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking (2017, methodology): “UAV123: This dataset [26] is created for Unmanned Aerial Vehicle (UAV) target tracking.”
Context-Aware Correlation Filter Tracking (2017, background): “Currently, CF-based trackers rank at the top of current benchmarks, such as OTB-100 [27], UAV123 [23], and VOT2015 [17], while remaining computationally efficient.”