BKU Team 2018

An implementation and a modified version of Real-world Anomaly Detection in Surveillance Videos (Sultani, Waqas and Chen) on Road_Accident dataset.

Road accident dataset consists of 796 videos under *.mp4 format (330 normal, 366 abnormal, 100 testing).

• Dataset link: updating
• C3D Extractor: Learning Spatiotemporal Features with 3D Convolutional Networks (Du Tran et al.).
• Extract C3D feature of video using Google Colab (this jupyter notebook)

Follow the instruction in the notebook to extract video feature.

Check this notebook Train_Test_Code to see the documentation as well as training/testing process.

• Keras 1.1.0
• Theano 0.9.0
• Python 3

Django web application. See WebApp directory for more details.

If you find any bug, or have some questions, feel free to contact any of these: Bien Do ([email protected]), Hoai Do ([email protected]), Dat Nguyen ([email protected]).

[1] W. Sultani, C. Chen, and M. Shah, “Real-world anomaly detection in surveillance videos,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2018.

[2] D. Tran, L. Bourdev, R. Fergus, et al., “Learning spatiotemporal features with 3d convolutional networks,” in The IEEE International Conference on Computer Vision (ICCV), Dec. 2015 .