Anomaly Detection

In this project, I have used YOLO v3 to detect anomalies. You can read the documentation of YOLO v3: https://pjreddie.com/darknet/yolo/

Also, you can read the documentation to train your model: https://github.com/mdv3101/darknet-yolov3

Approach

For this project, we can use YOLO or Tensorflow object detection API. But as I mentioned I have used YOLO v3 to train the model because it is the last official version that was released. For collecting the dataset I converted sample videos to images and then annotated them using labelImg.

Dependencies

At the first make sure that you installed Git & Python on your machine.

Then install these libraries:

• pip install opencv-python
• pip install opencv-contrib-python

Run

To run the project, go to the cloned repository's directory and then run detection.py file, Also you can run the code with the command prompt using this command: python detection.py. If you want to change the sample video you can change the path of the video in the detection file, line 4.

Other

• frame.py will convert sample videos to images and store them into a folder.

• anomaly.zip is the dataset with the annotation files.

• config folder includes trained model and configuration file.