CCTV Crime Annotation

Our fyp topic: Incorporate Interactive and transfer learning for automated video annotation

In this project, we have developed an application that integrates three models: object detection, human action recognition, and scene detection. The application is designed to analyze videos and provide insights based on these three models.

Setup

git clone https://github.com/atou0004/Crime_Annotation
cd Crime_Annotation
conda create -f fyp_environment.yaml
conda activate lastfyp

• Files & Folders to download from google drive

  Crime_Annotation/yolov7/best.pt

  Crime_Annotation/yolov7/traced_model.pt

  Crime_Annotation/traced_model.pt

  Crime_Annotation/yolov7/yolov7.pt

  Crime_Annotation/yolov7/runs (this whole folder (proof of tranfer learning done) is 28.3 GB)

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  Crime_Annotation/mmaction2/checkpoints/uniformerv2-large-p14-res336_clip-kinetics710-pre_u32_kinetics600-rgb_20221219-f984f5d2.pth

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  Crime_Annotation/Scene_Detection/RealESRGAN_x4plus.pth

  Crime_Annotation/Scene_Detection/wideresnet18_places365.pth.tar

  Crime_Annotation/Scene_Detection/image_super_resolution/wideresnet18_places365.pth.tar

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Notes in order for the gui to run successfully

There are absolute paths here and there that you need to change, in order for the app to run successfully

our working directory: "..../Crime_Annotation"

so just change the working directory (for both files) to: path/to/Crime_Annotation

gui.py

working_dir line 17

C:/Users/Tee/Desktop/FYP/GitFYP/Crime_Annotation/mmaction2 line 54 & 55

There isn't a need to change the name of the env, if the user is following the instructions of creating the env using the yaml file

the_cmd = 'conda run -n lastfyp python analyse_vid.py {}'.format(vid_file) line 60 (the name of the env this command is running on)

run_placesCNN_unified.py

file_name_category line 69

file_name_IO line 80

file_name_attribute line 93

file_name_W line 100

model_file line 124

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Notes about human_action_recognition model (mmaction2's UniFormerV2):

We ran our code on cpu, because our device on has 4GB of dedicated GPU memory, our har requires more than that. So if your GPU on your device has a at least 8GB of dedicated GPU memory, you can just comment line 16 and uncomment line 14 (for both analyse_vid and analyse_vid2), so that it uses that gpu, instead of cpu for a faster runtime.

analyse_vid.py is for listing out top5 actions in the video

analyse_vid2.py is for saving the top5 actions in a json file and saving the analysed videos

Here, these requirements, e.g., code standards, are not that strict as in the core package. Thus, developers from the community can implement their algorithms much more easily and efficiently in MMAction2. We appreciate all contributions from community to make MMAction2 greater.