ActVideo: Active Continuous Learning for Video Analytics

This project explores building an efficient edge video analytics platform by Active Continuous Learning to reduce human labeling and edge storage. It helps to judiciously select small but valuable partitions of video frames for continuous learning in video analytics.

🧑‍💻 This project is open for discussion and cooperation, please contact us if you are interested.

Repository Organization

├── core/                         # ⚡ Core Python package
│   ├── datasets/                 #    - Dataset loaders for video analytic tasks
│   ├── examplers/                #    - Exampler strategies for rehearsal-based learning
│   ├── metrics/                  #    - Training and testing tools for monitoring
│   ├── models/                   #    - Models for video analytic tasks
│   ├── retrainer/                #    - Continuous learning strategies and interfaces
│   ├── strategies/               #    - Sampling strategies for active learner
│   └── utils/                    #    - Utility functions and classes
│
├── exps/                         # 🔬 Examples of active continuous learning
│   ├── common.py                 #    - Interfaces for common experiment supports
│   ├── core50_nic_exp.py         #    - Experiment on CORe50 benchmark
│   └── video_detection_exp.py    #    - Experiment on video object detections
│
├── scripts/                      # 🛠️ Experiment scripts for running and testing
│
└── videos/                       # 📦 Prepared video datasets for testing

Installation

1. Install all dependencies from requirements.txt after creating your own python environment with python >= 3.8.0.

$ pip3 install -u -r requirements.txt

2. Download prepared datasets (release sooner), and then unzip on the projects root path.

$ tar -zxvf ***.tar.gz

Examples

We provide working examples for running:

• Classification on CORe50 datasets.

$ bash ./scripts/core50_***.sh

• Object Detecting on video datasets.
  • Dash Camera: $ bash ./scripts/dashcam_***.sh
  • Drone Video: $ bash ./scripts/drone_***.sh
  • Traffic Video: $ bash ./scripts/traffic_***.sh

Contributing

The codes are expanded on the Active-Learning-as-a-Service (ALaaS), and Video-Platform-as-a-Service (VPaaS).

This project is still under demo developing. Pull requests are more than welcome! If you have any questions please feel free to contact us.

Contact: Guanyu Gao, Lei Zhang, Huaizheng Zhang

Cite us

@inproceedings{zhang2022towards,
  title={Towards Data-Efficient Continuous Learning for Edge Video Analytics via Smart Caching},
  author={Zhang, Lei and Gao, Guanyu and Zhang, Huaizheng},
  booktitle={Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems},
  pages={1136--1140},
  year={2022}
}

@article{huang2022active,
  title={Active-Learning-as-a-Service: An Efficient MLOps System for Data-Centric AI},
  author={Huang, Yizheng and Zhang, Huaizheng and Li, Yuanming and Lau, Chiew Tong and You, Yang},
  journal={arXiv preprint arXiv:2207.09109},
  year={2022}
}

License

The project is available as open source under the terms of the Apache 2.0 License.