Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines
By Shuran Song, Jianxiong Xiao. 2013
298 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Martin Danelljan
- Luc Van Gool
- Ming-Hsuan Yang
- Goutam Bhat
- Dacheng Tao
- Fahad Shahbaz Khan
- Chao Ma
- Radu Timofte
Citations in the corpus
- RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, background): “The object types in PTB are rich and varied in the sense that it includes both deformable objects and relatively rigid objects that only have rotating and translating motions.”
- Layered RGBD scene flow estimation (2015, methodology): “We further test the methods using sequences from the RGBD tracking dataset [33].”
- Vision Meets Drones: A Challenge (2018, methodology): “In [44], a RGB-D tracking dataset is presented, which”
- RGBD Datasets: Past, Present and Future (2016, background): “The Princeton Tracking Benchmark [96] contains 100 videos of moving objects, such as dogs and toy cars.”
- GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild (2018, background): “set TColor-128 [16], long-term tracking dataset OxUvA [13], thermal tracking datasets PTB-TIR [22] and VOT-TIR [5], RGBD tracking dataset PTB [18] and high frame-rate track-”
- Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking (2016, background): “, depth sensors [25], and thermal infrared sensors [1].”
- Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, methodology): “the Princeton Tracking Benchmark [30], and the VOT2013 Challenge Benchmark [16].”
- The Seventh Visual Object Tracking VOT2019 Challenge Results (2019, background): “The most popular is Princeton Tracking Benchmark (PTB) [84], which contains 95 RGB-D video sequences of rigid and non-rigid objects recorded with Kinect.”
- Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models (2017, background): “Scene type: Different from the PTB (Song and Xiao 2013) dataset, all the videos in our dataset capture outdoor scenes.”