search

jx-zhong-for-academic-purpose / GCN-Anomaly-Detection

Source codes of our paper in CVPR 2019: Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection
231 stars 43 forks source link

UCSD Ped2 #28

Open joos2010kj opened 2 years ago

joos2010kj commented 2 years ago

Hello! Can I ask two questions?

1) how did you randomly select the videos for the train set, as there is no standard for randomness mentioned? Like, did you use numpy.random.seed(0)? If possible, could you provide a txt containing 60 anomaly videos and 40 normal videos used (6 abn 10 + 4norm 10) for the train?

2) So, is the training set just composed of 6 random abnormal videos and 4 random normal videos from UCSD Ped2? And the remaining 18 videos of abnormal and normal videos are used as test set?

I just keep having doubts about how the model can be trained on such a tiny data and have such an excellent performance b/c each of those 10 "videos" has between 120 and 180 frames only.

Thank you!

zimengxueying commented 1 year ago

Hi！How you ever solved he problem?