[vad_gan] Robust Anomaly Detection in Videos using Multilevel Representations

This project proposed an anomaly detection framework using multilevel information to identify anomaly objects in surveillance videos. Our research results in re-annotating the original UCSD Ped 1 dataset which is one of the most widely-used datasets in Video Anomaly Detection. You can find the new label set in relabeled_ped1.zip.

New label set for UCSD Ped 1

Folder structure:

relabeled_frames_anno
            |---- Test001_anno
            |            |----053.bmp
            |            |----053_anno.bmp
            |            |----054.bmp
            |            |----054_anno.bmp
            |            | ...
            |            |----069.bmp
            |            |----069_anno.bmp
            |
            |------Test002_anno
            |            |...
            |
            |------Test003_anno
            |            |...
            |          ...
            |------Test036_anno
                         |...
relabeled_frames_all
            |------Test001_gt
            |            |----001.bmp
            |            |----002.bmp
            |            | ...
            |            |----200.bmp
            |
            |------Test002_gt
            |            |...
            |          ...

            |------Test0036_gt
                         |...

Explanation:

relabeled_frames_anno: only consists of frames with label modifications. Each subfolder is a video and only videos with modifications are shown in this folder. Each frame has two *.bmp files:

• *_anno.bmp is the ground-truth of additional anomaly objects.
• *.bmp is the new ground-truth that is the OR image of the old UCSD Ped 1 ground-truth and the additional ground-truth (*_anno.bmp).

relabeled_frames_all: is the relabeled ground-truth of all frames in UCSD Ped 1 that are the new ground-truths from relabeled_frames_anno or the old ground-truths if frames are not re-annotated (not in relabeled_frames_anno).

If you are interested in the use of the new label set only, relabeled_frames_all is sufficient. When you use our source code or new label set, please cite our paper as well

Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Phung, "Robust Anomaly Detection in Videos using Multilevel Representations", in Proceedings of Thirty-third AAAI Conference on Artificial Intelligence (AAAI), Honolulu, USA, 27 January-01 February, 2019.