MOT-H: A Multi-Target Tracking Dataset Based on Horizontal View
Pages 338 - 344
Abstract
The computer vision field is quickly developing, including multiple object tracking, as the big data age approaches. The majority of the effort is focused on tracking methods while less attention is paid to the most important aspect, data. After an analysis of existing datasets, we find that they commonly ignore the breakpoint problem in tracking and have low image quality. Thus we present the dataset named Multiple Object Tracking on Horizontal view (MOT-H). MOT-H is meticulously annotated on crowded scenes from the horizontal view, with the primary goal of proving anti-jamming performance against complicated occlusion or even complete occlusion. The breakpoint issue is emphasized, which means the target object temporarily leaves the scene and returns after a while. The proposed MOT-H dataset has 10 sequences, 20,311 frames, and 337,440 annotation boxes in total, with all pictures having the resolution of 3840 × 2160 and being filmed at 30 frames per second (fps). We establish a fair benchmark for the future object tracking method development. The whole dataset can be found at: https://drive.google.com/drive/folders/1SCUJAdbqXQStyV-F2M9UyGfsuCaxR73a?usp=sharing.
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Index Terms
- MOT-H: A Multi-Target Tracking Dataset Based on Horizontal View
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Published In
October 2022
753 pages
ISBN:9781450397780
DOI:10.1145/3569966
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Published: 20 December 2022
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CSSE 2022
CSSE 2022: 2022 5th International Conference on Computer Science and Software Engineering
October 21 - 23, 2022
Guilin, China
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Overall Acceptance Rate 33 of 74 submissions, 45%
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