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ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection

Installation

  • Linux or macOS with Python ≥ 3.6
  • PyTorch ≥ 1.8. Install them together at pytorch.org to make sure of this. Note, please check PyTorch version matches that is required by Detectron2.
  • Detectron2: follow Detectron2 installation instructions.

Example conda environment setup

First, create a new conda environment. We suggest you to install pytorch 1.8.

conda create --name zbs python=3.8 -y
conda activate zbs
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia

Then, clone the repository locally and install dependencies:

# under your working directory
git clone [email protected]:CASIA-IVA-Lab/ZBS.git
git clone [email protected]:facebookresearch/detectron2.git
cd detectron2
pip install -e .
cd ../ZBS
pip install -r requirements.txt

Last, download the pretrained model, you can get more to check Detic's MODEL ZOO.

mkdir models
wget https://dl.fbaipublicfiles.com/detic/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth -O models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth

Data Preparation

Our experiments use CDnet 2014 and ABODA. Before starting processing, please download the (selected) datasets from the official websites and place or sim-link them under $ZBS_ROOT/datasets/.

$Detic_ROOT/datasets/
    metadata/
    cdnet2014/
    custom_video/
    ABODA/

metadata/ is our preprocessed meta-data (included in the repo). See the below section for details. Please follow the following instruction to pre-process individual datasets.

CDnet 2014 dataset

This dataset contains 11 video categories with 4 to 6 videos sequences in each category.

Download CDnet 2014 dataset from the website. We only need the dataset in this project:

cdnet2014/
    PTZ/
        continuousPan/
            groundtruth/
            ...
            temporalROI.txt/
        ...
        zoomInZoomOut/
            groundtruth/
            ...
            temporalROI.txt/
    ...
    turbulence/
        ...

ABODA

ABandoned Objects DAtaset (ABODA) is a new public dataset for abandoned object detection. ABODA comprises 11 sequences labeled with various real-application scenarios that are challenging for abandoned-object detection.

Download ABODA from the website:

ABODA/
    video1.avi
    ...
    video11.avi

Metadata

metadata/
    lvis_v1_train_cat_info.json
    coco_clip_a+cname.npy
    lvis_v1_clip_a+cname.npy
    o365_clip_a+cnamefix.npy
    oid_clip_a+cname.npy
    imagenet_lvis_wnid.txt
    Objects365_names_fix.csv

lvis_v1_train_cat_info.json is used by the Federated loss. This is created by

python tools/get_lvis_cat_info.py --ann datasets/lvis/lvis_v1_train.json

*_clip_a+cname.npy is the pre-computed CLIP embeddings for each datasets. They are created by (taking LVIS as an example)

python tools/dump_clip_features.py --ann datasets/lvis/lvis_v1_val.json --out_path metadata/lvis_v1_clip_a+cname.npy

Note we do not include the 21K class embeddings due to the large file size. To create it, run

python tools/dump_clip_features.py --ann datasets/lvis/lvis_v1_val_lvis-21k.json --out_path datasets/metadata/lvis-21k_clip_a+cname.npy

imagenet_lvis_wnid.txt is the list of matched classes between ImageNet-21K and LVIS.

Objects365_names_fix.csv is our manual fix of the Objects365 names.

Demo

Demo for CDnet 2014 dataset

To get the results on CDnet 2014 on a single GPU:

bash script/demo.sh cdnet

Demo for CDnet 2014 dataset

To evaluate the performance of ZBS on CDnet 2014 on a single GPU:

bash script/demo.sh test

Demo for ABODA

To evaluate ZBS on video on a single GPU:

bash script/demo.sh video datasets/ABODA/video1.avi