48 datasets found

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.

Object tracking by reconstruction with view-specific discriminative correlation filters.

The UAV123 dataset is a benchmark for object tracking.

The OTB100 dataset is a benchmark for object tracking.

The OTB50 dataset is a benchmark for object tracking.

The GOT-10K dataset is a large high-diversity benchmark for generic object tracking in the wild.

The proposed rectified binary convolutional networks (RBCNs) are used to improve the performance of 1-bit DCNNs for mobile and AI chips based applications.

The eighth visual object tracking VOT2020 challenge results.

Visual Object Tracking is one of the fundamental tasks in computer vision with applications ranging from human-computer interaction, surveillance, traffic flow monitoring and etc....

CenterPoseTrack is a joint detection and tracking method for category-level 6-DoF pose estimation of previously unseen object instances.

Objectron dataset contains 9 categories of objects with 15k video clips and over 4M frames annotated with 3D object bounding boxes, camera poses, sparse point clouds, and...

The dataset used in this paper is a real-world dataset for indoor spatial query evaluation, containing 40 objects moving on the second floor of the Haley Center on Auburn...

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

A large-scale benchmark called LiveVideos for the video object of interest segmentation task.

A new computer vision task named video object of interest segmentation (VOIS). Given a video and a target image of interest, the objective is to simultaneously segment and track...

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

Deep Neural Networks (DNNs) are universal function approximators providing state-of-the-art solutions on wide range of applications. Common perceptual tasks such as speech...