README.md

metadata

language:
  - en
pretty_name: CUHK-X — Small Model Track
tags:
  - multimodal
  - human-activity
  - action-recognition
  - depth
  - infrared
  - thermal
  - imu
  - radar
  - skeleton
task_categories:
  - video-classification

CUHK-X — Small Model Track

Multimodal human action recognition (classification). Given a multimodal clip, predict its action class (action_id, 0–39, 40 classes).

Repository layout

.
├── Training/
│   ├── class_mapping.csv     # action_id <-> action_name (40 classes)
│   └── data/
│       └── HAR.z01 … HAR.z08 + HAR.zip   # multi-volume zip
│           →  HAR/data/<modality>/<action>/<user>/<trial>/<files>
└── Testing/
    ├── data/
    │   └── small_model_track_test.zip    →  small_model_track_test/<id>/<modality>/<files>
    └── test_file/
        ├── test.csv              # path + empty `prediction` (to fill)
        └── sample_submission.csv # submission example

Labels

• Training labels live in the path: in HAR/data/<modality>/<action>/<user>/<trial>, the <action> (e.g. 0_Wash_face) is the class.
• Convert between action_name and action_id with class_mapping.csv.
• Test clips are anonymized (SM_test_XXXX); predict their action_id.

Modalities (6; no RGB, no raw Depth)

Modality	Type	Example file
Depth_Color	colorized depth (frames)	Depth_<datetime>_<idx>_Color.png
IR	infrared (frames)	IR_<datetime>_<idx>.png
Thermal	thermal (frames)	frame_000063.jpg
IMU	inertial sensor	*.csv
Radar	mmWave radar	radar_output_T<ts>.csv
Skeleton	skeleton	pose data +visualizations/

Sampling rates differ across modalities; not every clip has every modality.

Extracting the data

Training is a multi-volume zip (HAR.z01…HAR.z08 + HAR.zip; keep all volumes in one folder):

cd Training/data
zip -s 0 HAR.zip --out HAR_full.zip   # merge volumes (zip 3.0+)
unzip HAR_full.zip                    # -> HAR/data/<modality>/<action>/<user>/<trial>/...

7-Zip / WinRAR / double-click also handle split zips. Test set:

cd Testing/data && unzip small_model_track_test.zip   # -> small_model_track_test/<id>/<modality>/...

Submission

In Testing/test_file/test.csv, fill each row's prediction with the predicted action_id (0–39). See sample_submission.csv for the format.

Statistics

• 40 action classes
• 405 test clips

Quick start

import csv
id2name = {r["action_id"]: r["action_name"]
           for r in csv.DictReader(open("Training/class_mapping.csv", encoding="utf-8-sig"))}
# Training: the <action> folder in the path is the label, e.g.
#   HAR/data/IR/0_Wash_face/user10/4-2-1/  ->  action_name="0_Wash_face", action_id="0"