Our dataset, annotation instructions, dataset documentation and intended uses are published and you can download our dataset from here

Detection results of our method

Our method

YOLOv5-MOA

how to train

1. download the dataset from here
2. unzip the dataset and put it in the dataset folder
3. run python train.py to train the model with the default parameters defined in train.py. the usage is as same as original yolov5 method's usage. you can also change the parameters by yourself.

how to test

1. create a file named test.txt, and put the path of the test videos in it
2. run python test.py --val test.txt
3. the results will be saved in the video folder

Experiment

CPU: Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz GPU: GeForce RTX 3090

BSUV-NET-2.0

source code: BSUV-NET-2.0

Environment

1. python: 3.8.8
2. pytorch: 1.8.1
3. opencv: 4.0.1

Training Parameters

1. network: unetvgg16
2. inp_size: 600
3. empty_bg: manual
4. recent_bg: None
5. seg_ch: None
6. lr: 0.0001
7. weight_decay: 0.01
8. opt: adam
9. aug_ioa: None

MOBILE-VOD

source code: MOBILE-VOD

Environment

1. python: 3.8.12
2. pytorch: 1.11.0
3. opencv-python: 4.5.5.64

Training Parameters

1. network: mvod_lstm1
2. lr: 0.003
3. momentum: 0.9
4. weight_decay: 0.0005
5. gamma: 0.1

Opencv Background Subtract

Run python precision_recall_tro.py --algo <k> for <k> = MOG2, KNN, ... to compute the results for each fold. This code will save the results to result.txt.

Environment

1. python: 3.8.8
2. opencv-contrib-python: 4.5.5.64

MOG2

opencv::createBackgroundSubtractorMOG2()

1. history: 300
2. varThreshold: 100
3. detectShadow: false

KNN

opencv::createBackgroundSubtractorKNN()

1. history: 300
2. varThreshold: 100
3. detectShadow: false

MOG

opencv::bgsegm::createBackgroundSubtractorMOG()

• default

CNT

opencv::bgsegm::createBackgroundSubtractorCNT()

1. setIsParallel
2. setUseHistory
3. setMinPixelStability(1)
4. setMaxPixelStability(4)

GMG

cv::bgsegm::BackgroundSubtractorGMG

1. setNumFrames(5)
2. setUpdateBackgroundModel(True)

GSOC

cv::bgsegm::BackgroundSubtractorGSOC

1. history: 300
2. varThreshold: 100

LSBP

cv::bgsegm::BackgroundSubtractorLSBP

• default

VIBE

Run python vibe.py to compute the results for each fold. This code will save the results to result.txt.

1. num_sam: 20
2. min_match: 2
3. radiu: 20
4. rand_sa: 16