Human-Centric Spatio-Temporal Video Grounding With Visual Transformers

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 12; pp. 8238 - 8249
• Main Authors: Tang, Zongheng, Liao, Yue, Liu, Si, Li, Guanbin, Jin, Xiaojie, Jiang, Hongxu, Yu, Qian, Xu, Dong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: dataset; Datasets; Electron tubes; Grounding; Localization; Location awareness; Power transformers; Spatial temporal resolution; Spatio-temporal grounding; transformer; Transformers; Video; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2021.3085907

In this work, we introduce a novel task - Human-centric Spatio-Temporal Video Grounding (HC-STVG). Unlike the existing referring expression tasks in images or videos, by focusing on humans, HC-STVG aims to localize a spatio-temporal tube of the target person from an untrimmed video based on a given textural description. This task is useful, especially for healthcare and security related applications, where the surveillance videos can be extremely long but only a specific person during a specific period is concerned. HC-STVG is a video grounding task that requires both spatial (where) and temporal (when) localization. Unfortunately, the existing grounding methods cannot handle this task well. We tackle this task by proposing an effective baseline method named Spatio-Temporal Grounding with Visual Transformers (STGVT), which utilizes Visual Transformers to extract cross-modal representations for video-sentence matching and temporal localization. To facilitate this task, we also contribute an HC-STVG datasetThe new dataset is available at https://github.com/tzhhhh123/HC-STVG . consisting of 5,660 video-sentence pairs on complex multi-person scenes. Specifically, each video lasts for 20 seconds, pairing with a natural query sentence with an average of 17.25 words. Extensive experiments are conducted on this dataset, demonstrating that the newly-proposed method outperforms the existing baseline methods.