Enhancing Feature Representation for Anomaly Detection via Local-and-Global Temporal Relations and a Multi-Stage Memory

PaperID-347 for PRCV 2023

Code Environment

conda env create -f environment.yaml

Data Preparation

ShanghaiTech and UCF-Crime

You can refer to the work 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021] https://github.com/tianyu0207/RTFM#setup for I3D features of ShanghaiTech and UCF-Crime datasets.

Inference

ShanghaiTech

First, unzip the compressed files in the folder 'sh_ckpt' into the checkpoint file 'sh_model.pkl'

zip -s 0 sh_ckpt/sh_model.zip --out sh_ckpt/ckpt.zip
unzip sh_ckpt/ckpt.zip 

Then, test the model

python test.py -d shanghai -p /path/of/the/root/of/test/I3D/features -c sh_model.pkl

UCF-Crime

First, unzip the compressed files in the folder 'ucf_ckpt' into the checkpoint file 'ucf_model.pkl'

zip -s 0 ucf_ckpt/ucf_model.zip --out ucf_ckpt/ckpt.zip
unzip ucf_ckpt/ckpt.zip

Then, test the model

python test.py -d ucf -p /path/of/the/root/of/test/I3D/features -c ucf_model.pkl

Supplementary Material

Supplementary material can be found in 0347_supp.pdf