Official implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" (ICCV Workshops 2021: RSL-CV).

Related tags

Deep LearningSTEAL
Overview

Official PyTorch implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection"

This is the implementation of the paper "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" (ICCV Workshops 2021: RSL-CV).

Paper || Presentation Video

Dependencies

  • Python 3.6
  • PyTorch = 1.7.0
  • Numpy
  • Sklearn

Datasets

Download the datasets into dataset folder, like ./dataset/ped2/, ./dataset/avenue/, ./dataset/shanghai/

Training

git clone https://github.com/aseuteurideu/STEAL
  • Training baseline
python train.py --dataset_type ped2
  • Training STEAL Net
python train.py --dataset_type ped2 --pseudo_anomaly_jump 0.2 --jump 2 3 4 5

Select --dataset_type from ped2, avenue, or shanghai.

For more details, check train.py

Pre-trained model

Model Dataset AUC Weight
Baseline Ped2 92.5% [ drive ]
Baseline Avenue 81.5% [ drive ]
Baseline ShanghaiTech 71.3% [ drive ]
STEAL Net Ped2 98.4% [ drive ]
STEAL Net Avenue 87.1% [ drive ]
STEAL Net ShanghaiTech 73.7% [ drive ]

Evaluation

  • Test the model
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth
  • Test the model and save result image
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth --img_dir folder_path_to_save_image_results
  • Test the model and generate demonstration video frames
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth --vid_dir folder_path_to_save_video_results

Then compile the frames into video. For example, to compile the first video in ubuntu:

ffmpeg -framerate 10 -i frame_00_%04d.png -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p video_00.mp4

Bibtex

@InProceedings{Astrid_2021_ICCV,
    author    = {Astrid, Marcella and Zaheer, Muhammad Zaigham and Lee, Seung-Ik},
    title     = {Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month     = {October},
    year      = {2021},
    pages     = {207-214}
}

Acknowledgement

The code is built on top of code provided by Park et al. [ github ] and Gong et al. [ github ]

Owner
Marcella Astrid
PhD candidate at University of Science and Technology, ETRI campus, South Korea
Marcella Astrid
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
The LaTeX and Python code for generating the paper, experiments' results and visualizations reported in each paper is available (whenever possible) in the paper's directory

This repository contains the software implementation of most algorithms used or developed in my research. The LaTeX and Python code for generating the

João Fonseca 3 Jan 03, 2023
The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

Directed Graph Contrastive Learning Paper | Poster | Supplementary The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL). In this

Tong Zekun 28 Jan 08, 2023
The code for our paper Semi-Supervised Learning with Multi-Head Co-Training

Semi-Supervised Learning with Multi-Head Co-Training (PyTorch) Abstract Co-training, extended from self-training, is one of the frameworks for semi-su

cmc 6 Dec 04, 2022
This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). More details can be found at the project page. Framework Grounding Physical

Zhenfang Chen 31 Jan 06, 2023
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines

A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines Understanding the results of deep neural networks is

Johan van den Heuvel 2 Dec 13, 2021
Data pipelines for both TensorFlow and PyTorch!

rapidnlp-datasets Data pipelines for both TensorFlow and PyTorch ! If you want to load public datasets, try: tensorflow/datasets huggingface/datasets

1 Dec 08, 2021
Official implementation of VaxNeRF (Voxel-Accelearated NeRF).

VaxNeRF Paper | Google Colab This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF). VaxNeRF provides very fast training and slightl

naruya 132 Nov 21, 2022
Runtime type annotations for the shape, dtype etc. of PyTorch Tensors.

torchtyping Type annotations for a tensor's shape, dtype, names, ... Turn this: def batch_outer_product(x: torch.Tensor, y: torch.Tensor) - torch.Ten

Patrick Kidger 1.2k Jan 03, 2023
A scientific and useful toolbox, which contains practical and effective long-tail related tricks with extensive experimental results

Bag of tricks for long-tailed visual recognition with deep convolutional neural networks This repository is the official PyTorch implementation of AAA

Yong-Shun Zhang 181 Dec 28, 2022
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations

Transfer-Learning-in-Reinforcement-Learning Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations Final Report Tra

Trung Hieu Tran 4 Oct 17, 2022
End-to-End Speech Processing Toolkit

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 1.9.0 ubuntu20/python3.9/pip ubuntu20/python3.8/p

ESPnet 5.9k Jan 04, 2023
Simple node deletion tool for onnx.

snd4onnx Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs

Katsuya Hyodo 6 May 15, 2022
《LXMERT: Learning Cross-Modality Encoder Representations from Transformers》(EMNLP 2020)

The Most Important Thing. Our code is developed based on: LXMERT: Learning Cross-Modality Encoder Representations from Transformers

53 Dec 16, 2022
official code for dynamic convolution decomposition

Revisiting Dynamic Convolution via Matrix Decomposition (ICLR 2021) A pytorch implementation of DCD. If you use this code in your research please cons

Yunsheng Li 110 Nov 23, 2022
Ranger deep learning optimizer rewrite to use newest components

Ranger21 - integrating the latest deep learning components into a single optimizer Ranger deep learning optimizer rewrite to use newest components Ran

Less Wright 266 Dec 28, 2022
[CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.

[CVPR2022] Thin-Plate Spline Motion Model for Image Animation Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"

yoyo-nb 1.4k Dec 30, 2022
A unified framework to jointly model images, text, and human attention traces.

connect-caption-and-trace This repository contains the reference code for our paper Connecting What to Say With Where to Look by Modeling Human Attent

Meta Research 73 Oct 24, 2022
Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.

Graph-Based Local Trajectory Planner The graph-based local trajectory planner is python-based and comes with open interfaces as well as debug, visuali

TUM - Institute of Automotive Technology 160 Jan 04, 2023
Pytorch implementation for A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose

A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose Paper | Website | Data A-NeRF: Articulated Neural Radiance F

Shih-Yang Su 172 Dec 22, 2022