UNISV-Dataset
The dataset introduces 1,200 samples of nighttime infrared surveillance video behavior recognition. It uses raw, unedited footage covering 10 distinct actions, each with 120 samples, following the UCF‑101 naming convention (e.g., v_DoubleWave_g01_c01). All videos were recorded outdoors at night in realistic surveillance locations such as parking lots, gardens, and alleys, involving 15 participants of varied height and build.
Dataset description and usage context
UNISV‑Dataset Overview
Dataset Description
- Sample Count: 1,200 samples.
- Action Classes: 10 different behaviors, each with 120 samples.
- Video Characteristics: Raw or randomly edited original videos, most without people. Filenames follow the UCF‑101 format, e.g., "v_DoubleWave_g01_c01" where "v_DoubleWave" denotes the action class, and "g01" and "c01" indicate location and sample index.
- Scene Setup: All videos were recorded outdoors at night, mirroring key positions of real surveillance cameras such as parking lots, gardens, and alleys.
- Participants: 15 volunteers with diverse heights and body types.
Action Classes
- Class List: "Double Wave," "Wave Hand," "Walk," "Jump," "Squat," "Jogging," "Push People," "Shake Hands," "Embrace," and "Fight."
- Participant Count: Each class involves 6 participants, totaling 15 participants overall.
- Video Samples per Class: 120 videos per class, 1,200 videos in total.
- Video Specs: 10 fps, resolution 480 × 248 px.
Citation Information
If you use this dataset, please cite:
Feng Z, Wang X, Zhou J, et al. MDJ: A Multi‑Scale Difference Joint Keyframe Extraction Algorithm for Infrared Surveillance Video Action Recognition[J]. Digital Signal Processing, 2024: 104469. https://doi.org/10.1016/j.dsp.2024.104469.
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