VideoCube

A general benchmark for visual tracking intelligence evaluation

VideoCube is a high-quality and large-scale benchmark to create a challenging real-world experimental environment for Global Instance Tracking (GIT).

Task

Global Instance Tracking (GIT) task aims to model the fundamental visual function of humans for motion perception without any assumptions about camera or motion consistency.

Key Features

Large-Scale

VideoCube contains 500 video segments of real-world moving objects and over 7.4 million labeled bounding boxes. We guarantee that each video contains at least 4008 frames, and the average frame length in VideoCube is around 14920.

Multiple Collection Dimension

The collection of VideoCube is based on six dimensions to describe the spatio-temporal relationship and causal relationship of film narrative, which provides an extensive dataset for the novel GIT task.

Comprehensive Attribute Selection

VideoCube provides 12 attributes for each frame to reflect the challenging situations in actual applications, and implement a more elaborate reference for the performance analysis.

Scientific Evaluation

VideoCube provides classical metrics and novel metrics for to evaluation algorithms. Besides, this benchmark also provides human baseline to measure the intelligence level of existing methods.

Latest News

Publications

Publication

Global Instance Tracking: Locating Target More Like Humans.
S. Hu, X. Zhao*, L. Huang and K. Huang (*corresponding author)
IEEE Transactions on Pattern Analysis and Machine Intelligence
[DOI] [PDF] [BibTex]

Please cite our IEEE TPAMI paper if VideoCube helps your research.

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