Abnormal Human Behaviors Detection/ Road Accident Detection From Surveillance Videos/ Real-World Anomaly Detection in Surveillance Videos/ C3D Feature Extraction
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).
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.
Django web application. See WebApp directory for more details.
File/Directory | Decscription |
---|---|
C3D | Extract C3D video feature |
Scripts | Python, Matlab ultility scripts |
Temporal Annotation | Groudtruth annotation of testing videos |
Makefile.config | Configuration file to build C3D Caffe model |
Train/Test Code | Jupyter notebook for Traning/Testing process |
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 .