YOWOF

You Only Watch One Frame (YOWOF) for Online Spatio-Temporal Action Detection

The whole pipeline of YOWOF：

Online inference of YOWOF：

Paper: coming soon ... [under review]

Requirements

We recommend you to use Anaconda to create a conda environment:

conda create -n yowof python=3.6

Then, activate the environment:

conda activate yowof

Requirements:

pip install -r requirements.txt

Dataset

UCF101-24:

Google drive

Link: https://drive.google.com/file/d/1Dwh90pRi7uGkH5qLRjQIFiEmMJrAog5J/view?usp=sharing

BaiduYun Disk

Link: https://pan.baidu.com/s/11GZvbV0oAzBhNDVKXsVGKg

Password: hmu6

AVA

You can use instructions from here to prepare AVA dataset.

Visualization

Experiment

Frame-mAP@0.5IoU & Video-mAP@0.5IoU on UCF24

Model	Clip	FPS	GFLOPs	F-mAP	V-mAP	Weight
YOWOF-R18	16	225	4.9	83.3	49.1	ckpt

Frame-mAP@0.5 IoU on AVA_v2.2

Model	Clip	FPS	GFLOPs	Params	mAP	Weight
YOWOF-R18	16	220	5.0	23.9 M	18.1	ckpt
YOWOF-R50	16	125	11.1	50.5 M	20.7	ckpt

Train YOWOF

Train yowof-r18 on UCF24

python train.py --cuda -d ucf24 -v yowof-r18 --num_workers 4 --eval_epoch 1 --eval

or you can just run the script:

sh train_ucf.sh

Train yowof-r50 on AVA_v2.2

python train.py --cuda -d ava_v2.2 -v yowof-r50 --num_workers 4 --eval_epoch 1 --eval

or you can just run the script:

sh train_ava.sh

Test YOWOF

Test yowof-r18 on UCF24

run yowof with clip inference mode

python test.py --cuda -d ucf24 -v yowof-r18 --weight path/to/weight --inf_mode clip --show

run yowof with stream inference mode

python test.py --cuda -d ucf24 -v yowof-r18 --weight path/to/weight --inf_mode stream --show

Test yowof-r50 on AVA_v2.2

run yowof with clip inference mode

python test.py --cuda -d ava_v2.2 -v yowof-r50 --weight path/to/weight --inf_mode clip --show

run yowof with stream inference mode

python test.py --cuda -d ava_v2.2 -v yowof-r50 --weight path/to/weight --inf_mode stream --show

Evaluate YOWOF

on UCF24

# frame mAP
python eval.py \
        --cuda \
        -d ucf24 \
        -v yowof-r18 \
        --weight path/to/weight \
        --cal_frame_mAP

Our Frame-mAP@0.5IoU achieved by YOWOF-R18 on UCF24:

AP: 79.05% (1)
AP: 97.07% (10)
AP: 83.96% (11)
AP: 64.24% (12)
AP: 72.87% (13)
AP: 94.46% (14)
AP: 88.25% (15)
AP: 93.32% (16)
AP: 78.41% (17)
AP: 94.25% (18)
AP: 97.36% (19)
AP: 45.69% (2)
AP: 96.13% (20)
AP: 82.96% (21)
AP: 79.45% (22)
AP: 54.20% (23)
AP: 92.01% (24)
AP: 88.81% (3)
AP: 78.68% (4)
AP: 70.28% (5)
AP: 95.14% (6)
AP: 92.11% (7)
AP: 89.49% (8)
AP: 91.70% (9)
mAP: 83.33%

# video mAP
python eval.py \
        --cuda \
        -d ucf24 \
        -v yowof-r18 \
        --weight path/to/weight \
        --cal_video_mAP

Our Video-mAP@0.5IoU achieved by YOWOF-R18 on UCF24:

-------------------------------
V-mAP @ 0.05 IoU:
--Per AP:  [87.71, 93.6, 68.66, 96.26, 79.63, 100.0, 82.72, 100.0, 93.36, 96.08, 44.8, 91.01, 91.87, 99.76, 23.33, 98.87, 90.87, 96.55, 91.46, 65.01, 72.97, 49.67, 86.4, 87.96]
--mAP:  82.86
-------------------------------
V-mAP @ 0.1 IoU:
--Per AP:  [87.71, 91.01, 68.66, 93.73, 79.63, 100.0, 82.72, 100.0, 93.36, 96.08, 44.8, 87.62, 91.87, 99.76, 23.33, 98.87, 90.87, 96.55, 91.46, 63.46, 70.97, 49.67, 57.29, 87.96]
--mAP:  81.14
-------------------------------
V-mAP @ 0.2 IoU:
--Per AP:  [58.88, 84.02, 64.38, 78.9, 38.04, 100.0, 82.72, 100.0, 82.5, 96.08, 44.8, 84.75, 91.87, 99.76, 22.2, 98.87, 90.87, 96.55, 91.46, 61.48, 49.51, 48.3, 32.85, 87.96]
--mAP:  74.45
-------------------------------
V-mAP @ 0.3 IoU:
--Per AP:  [8.66, 28.1, 64.38, 69.77, 12.2, 84.79, 77.96, 100.0, 82.5, 92.68, 44.8, 72.98, 75.96, 99.76, 16.23, 98.87, 90.87, 96.55, 91.46, 51.32, 30.98, 43.29, 3.24, 80.77]
--mAP:  63.25
-------------------------------
V-mAP @ 0.5 IoU:
--Per AP:  [0.0, 1.75, 53.12, 35.56, 0.66, 40.99, 62.67, 90.6, 49.47, 89.94, 44.8, 57.89, 48.27, 99.76, 4.81, 98.87, 85.47, 89.53, 87.81, 44.64, 0.45, 20.97, 0.0, 69.87]
--mAP:  49.08
-------------------------------
V-mAP @ 0.75 IoU:
--Per AP:  [0.0, 0.0, 22.6, 0.0, 0.0, 1.11, 27.9, 64.31, 11.68, 42.35, 21.51, 34.02, 0.8, 40.54, 0.92, 80.21, 44.81, 19.75, 80.34, 18.34, 0.0, 0.92, 0.0, 35.32]
--mAP:  22.81

on AVA_v2.2

python eval.py --cuda -d ucf24 -v yowof-r50 --weight path/to/weight

Our SOTA result achieved by YOWOF-R50 on AVA_v2.2:

  'AP@0.5IOU/answer phone': 0.701,
  'AP@0.5IOU/bend/bow (at the waist)': 0.358,
  'AP@0.5IOU/carry/hold (an object)': 0.496,
  'AP@0.5IOU/climb (e.g., a mountain)': 0.007,
  'AP@0.5IOU/close (e.g., a door, a box)': 0.087,
  'AP@0.5IOU/crouch/kneel': 0.178,
  'AP@0.5IOU/cut': 0.026,
  'AP@0.5IOU/dance': 0.295,
  'AP@0.5IOU/dress/put on clothing': 0.006,
  'AP@0.5IOU/drink': 0.223,
  'P@0.5IOU/drive (e.g., a car, a truck)': 0.531,
  'AP@0.5IOU/eat': 0.209,
  'AP@0.5IOU/enter': 0.0345,
  'AP@0.5IOU/fall down': 0.098,
  'AP@0.5IOU/fight/hit (a person)': 0.348,
  'AP@0.5IOU/get up': 0.092,
  'AP@0.5IOU/give/serve (an object) to (a person)': 0.057,
  'AP@0.5IOU/grab (a person)': 0.046,
  'AP@0.5IOU/hand clap': 0.019,
  'AP@0.5IOU/hand shake': 0.014,
  'AP@0.5IOU/hand wave': 0.004,
  'AP@0.5IOU/hit (an object)': 0.006,
  'AP@0.5IOU/hug (a person)': 0.217,
  'AP@0.5IOU/jump/leap': 0.093,
  'AP@0.5IOU/kiss (a person)': 0.398,
  'AP@0.5IOU/lie/sleep': 0.581,
  'AP@0.5IOU/lift (a person)': 0.027,
  'AP@0.5IOU/lift/pick up': 0.021,
  'AP@0.5IOU/listen (e.g., to music)': 0.014,
  'AP@0.5IOU/listen to (a person)': 0.565,
  'AP@0.5IOU/martial art': 0.329,
  'AP@0.5IOU/open (e.g., a window, a car door)': 0.133,
  'AP@0.5IOU/play musical instrument': 0.282,
  'AP@0.5IOU/point to (an object)': 0.001,
  'AP@0.5IOU/pull (an object)': 0.022,
  'AP@0.5IOU/push (an object)': 0.016,
  'AP@0.5IOU/push (another person)': 0.087,
  'AP@0.5IOU/put down': 0.030,
  'AP@0.5IOU/read': 0.270,
  'AP@0.5IOU/ride (e.g., a bike, a car, a horse)': 0.251,
  'AP@0.5IOU/run/jog': 0.283,
  'AP@0.5IOU/sail boat': 0.250,
  'AP@0.5IOU/shoot': 0.005,
  'AP@0.5IOU/sing to (e.g., self, a person, a group)': 0.104,
  'AP@0.5IOU/sit': 0.790,
  'AP@0.5IOU/smoke': 0.040,
  'AP@0.5IOU/stand': 0.778,
  'AP@0.5IOU/swim': 0.227,
  'AP@0.5IOU/take (an object) from (a person)': 0.047,
  'AP@0.5IOU/take a photo': 0.060,
  'AP@0.5IOU/talk to (e.g., self, a person, a group)': 0.688,
  'AP@0.5IOU/text on/look at a cellphone': 0.061,
  'AP@0.5IOU/throw': 0.007,
  'AP@0.5IOU/touch (an object)': 0.388,
  'AP@0.5IOU/turn (e.g., a screwdriver)': 0.014,
  'AP@0.5IOU/walk': 0.542,
  'AP@0.5IOU/watch (a person)': 0.663,
  'AP@0.5IOU/watch (e.g., TV)': 0.177,
  'AP@0.5IOU/work on a computer': 0.099,
  'AP@0.5IOU/write': 0.041,
  'mAP@0.5IOU': 0.207

Detect AVA video

python test_video_ava.py --cuda -d ucf24 -v yowof-r50 --weight path/to/weight --video ava/video/name

Demo

detection action instances with UCF24 labels

We provide some test videos of UCF24 in dataset/demo/ucf24_demo/.

python demo.py --cuda -d ucf24 -v yowof-r18 --weight path/to/weight --video ./dataset/demo/ucf24_demo/v_Basketball_g01_c02.mp4

detection action instances with AVA labels

python demo.py --cuda -d ava_v2.2 -v yowof-r50 --weight path/to/weight --video path/to/video

Name		Name	Last commit message	Last commit date
Latest commit History 728 Commits
config		config
dataset		dataset
evaluator		evaluator
img_files		img_files
models		models
utils		utils
.gitignore		.gitignore
README.md		README.md
demo.py		demo.py
eval.py		eval.py
requirements.txt		requirements.txt
test.py		test.py
test_video_ava.py		test_video_ava.py
train.py		train.py
train_ava.sh		train_ava.sh
train_ddp.sh		train_ddp.sh
train_ucf.sh		train_ucf.sh

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YOWOF

Requirements

Dataset

UCF101-24:

AVA

Visualization

Experiment

Train YOWOF

Train yowof-r18 on UCF24

Train yowof-r50 on AVA_v2.2

Test YOWOF

Test yowof-r18 on UCF24

Test yowof-r50 on AVA_v2.2

Evaluate YOWOF

Detect AVA video

Demo

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