ADAG (Activity Detector and Alert Generator) aims to take real-time videos from CCTV as an input and pass it to the CNN model created with the help of transfer learning and detect ‘Shoplifting’, ‘Robbery’ or ’Break-In’ in the store and notify it to the owners as soon as it occurs. Finally the main motive is to provide a system that detects suspicious activities without human intervention and generates alert, thus making a huge revolution in today’s surveillance system.
gh repo clone OmRajpurkar/Alert-Generation-on-Detection-of-Suspicious-Activity-using-Transfer-Learning
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Download the model and place it inside ‘videoClassification’ directory.
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Create a new Virtual Environment and Activate it.
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To save the dataset images create a directory named 'data' inside the 'videoClassification' directory. Inside the 'data' directory create 3 sub-directories each for 'Shoplifting', 'Robbery' and 'Break-In'. Store the training images for the respective classes inside those directories.
$ pip install -r requirements.txt$ python train.py --dataset data --model model --label-bin label_bin --epochs 80$ python predict_video.py -m model -l label_bin -i /videoClassification/Shoplifting.mp4 -o ShopliftingOutput.avi$ python predict_video_realtime.py -m model -l label_bin -o outputO. M. Rajpurkar, S. S. Kamble, J. P. Nandagiri and A. V. Nimkar, "Alert Generation on Detection of Suspicious Activity Using Transfer Learning," 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2020, pp. 1-7, doi: 10.1109/ICCCNT49239.2020.9225263.
IEEE Xplore Link: https://ieeexplore.ieee.org/document/9225263