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2024videoactionrec

https://github.com/visualmedialaboratory/2024videoactionrec

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: arxiv.org
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    Academic email domains
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    Institutional organization owner
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  • ○
    Scientific vocabulary similarity
    Low similarity (6.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: VisualMediaLaboratory
  • License: apache-2.0
  • Language: Python
  • Default Branch: hnu_action_demo
  • Size: 68.3 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 2
  • Open Issues: 1
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Code of conduct Citation

README.md

쓰레기 불법 투기 감지를 위한 영상처리 알고리즘 연구

Introduction

Features

  • 행동 인식(Action Recognition) 신경망을 활용한 불법 쓰레기 무단 투기 행동 감지
  • MMaction2에서 지원하는 Kinetics-400으로 사전학습된 I3D(Inflated 3D ConvNet) 신경망을 미세 조정(Fine Tuning) 학습을 진행하여 성능 개선
  • AI-HUB에서 제공하는 이상행동 CCTV 영상의 투기(dump) 데이터 셋을 사용하여 미세 조정 학습 진행 ### I3D(Inflated 3D ConvNet)

image

  • 비디오 데이터의 행동 인식을 위해 설계된 딥러닝 모델로, 2D CNN을 기반으로 3D CNN으로 확장한 모델
  • 각 컨볼루션 필터가 2D 대신 3D로 작동하며, 동영상 데이터를 처리할 수 있도록 시간 축 정보도 포함됨
  • RGB 데이터와 Optical Flow 데이터를 3D Conv로 처리하여 시간 흐름에 따른 공간적 변화를 학습

image

  • Google의 Inception V1 아키텍처를 3D로 확장하여 설계되었음

Installation

가상환경 생성 python3.8 -m venv venv source ./venv/bin/activate pip install -U pip

Dependencies

  • CUDA11.8
  • Python3.8
  • pytorch 2.0.0+cu118
  • torchvision 0.15.1+cu118

mmaction 관련 패키지 설치 pip install -U openmim mim install mmengine mim install "mmcv==2.1.0" mim install mmdet

Project build git clone "this repository" project cd project pip install -v -e .

Run

Video input Video output download link (weight file) : i3dimagenet-pretrained-r50-heavy8xb8-32x2x1-100ekinetics400-rgbtrainedbyhnuepoch40.pth

download link (demo video, dump) : i3dimagenet-pretrained-r50-heavy8xb8-32x2x1-100ekinetics400-rgbtrainedbyhnuepoch40.pth python demo/demo_visualize.py configs/recognition/i3d/i3d_dense_trained_by_hnu.py \ i3d_imagenet-pretrained-r50-heavy_8xb8-32x2x1-100e_kinetics400-rgb_trained_by_hnu_epoch_40.pth \ demo/167-3_cam02_dump02_place04_day_summer.mp4 tools/data/kinetics/label_map_hnu_label.txt \ --out-filename output.mp4

| 사진1 | 사진2 | |:-----------------:|:---------------:| | 투기행위 신뢰도 : 0.78 (야간) | 투기행위 신뢰도 : 0.98 (주간) | | 사진3 | 사진4 | | 투기행위 신뢰도 : 0.91 (주간) | 투기행위 신뢰도 : 0.92 (주간) |

Related linkes

  • I3D : Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMAction2: OpenMMLab Video Action Recognition toolbox and benchmark.

Owner

  • Login: VisualMediaLaboratory
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMAction2 Contributors"
title: "OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark"
date-released: 2020-07-21
url: "https://github.com/open-mmlab/mmaction2"
license: Apache-2.0

GitHub Events

Total
  • Pull request event: 5
  • Fork event: 2
  • Create event: 2
Last Year
  • Pull request event: 5
  • Fork event: 2
  • Create event: 2

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