16 datasets found

The Visual Tracking Benchmark (VTB) is a collection of 50 videos.

The OTB-100 and OTB-50 datasets are used in the experiments to evaluate the performance of the Convolutional Regression framework for visual tracking.

The Convolutional Regression framework for visual tracking is proposed in this paper. The framework uses a single convolution layer to learn a regression model for visual tracking.

The VOT2015/VOT2017 datasets are used for visual tracking evaluation.

The OTB-2013/OTB-2015 datasets are used for visual tracking evaluation.

Visual tracking is one of the most fundamental topics in computer vision. It has great demands in many public occasions such as surveillance system, self-driving cars, etc.

The visual object tracking vot2016 challenge results

The visual object tracking vot2015 challenge results

The OTB100 dataset is an extension of the OTB50 dataset, containing 100 videos with 1000 frames each.

Visual object tracking is a fundamental problem in many aspects such as visual analysis, automatic driving, pose tracking, robotics, and more.

The dataset used in the paper is the OTB100, VOT2016 and VOT2017 datasets for visual tracking.

The TrackingNet dataset is a benchmark for visual tracking, containing 511 video sequences with varying difficulties.

The LaSOT dataset is a large-scale benchmark for visual tracking, containing 1400 video sequences with varying difficulties.

The UAV123 dataset is a benchmark for visual tracking, containing 123 video sequences with varying difficulties.

The VOT2018 dataset contains 60 videos and 100 frames for visual object tracking.

The OTB100 dataset contains 100 videos and 60 frames for visual object tracking.