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Awesome-Multi-Camera-Network
Multi-camera Network research resources
https://github.com/Jason-cs18/Awesome-Multi-Camera-Network
Last synced: about 10 hours ago
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Outline
- style transfer - resource learning and I only found two related papers ([SpotTune, *CVPR'19*, Citation=159](https://openaccess.thecvf.com/content_CVPR_2019/papers/Guo_SpotTune_Transfer_Learning_Through_Adaptive_Fine-Tuning_CVPR_2019_paper.pdf) and [Budget-Aware Adapters, *ICCV'19*, Citation=10](https://openaccess.thecvf.com/content_ICCV_2019/papers/Berriel_Budget-Aware_Adapters_for_Multi-Domain_Learning_ICCV_2019_paper.pdf)), which are not based on detection architectures and suitable for all CNN models.
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Book and Survey
- Camera Networks: The Acquisition and Analysis of Videos over Wide Areas (Synthesis Lectures on Computer Vision). 2012.
- M.Valera et al. Intelligent distributed surveillance systems: a review. 2005.
- Ye et al. Wireless Video Surveillance: A Survey. 2013.
- Zhang et al. Deep Learning in Mobile and Wireless Networking: A Survey. IEEE TRANS 2019.
- Zhang et al. Deep Learning in Mobile and Wireless Networking: A Survey. IEEE TRANS 2019.
- Camera Networks: The Acquisition and Analysis of Videos over Wide Areas (Synthesis Lectures on Computer Vision). 2012.
- Wang et al. Intelligent multi-camera video surveillance: a review. 2012.
- Multi-Camera Networks: Principles and Applications. 2005.
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Researchers, Workshops and Courses
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Researchers (organization and research interests)
- Matthias Boehm (Graz University of Technology, Austria) - data management and deep learning based data analytics
- Arun Kumar (University of California San Diego, USA) - data management and deep learning based data analytics
- Ganesh Ananthanarayanan (Microsoft Research, USA) - live video analytics, distributed computing
- Feng Qian (University of Minnesota Twin Cities, USA) - video streaming
- Juncheng Jiang (The University of Chicago, USA) - video streaming
- Ravi Netravali (Princeton, USA) - edge video AI
- Fengyuan Xu (Nanjing University, China) - the Internet of Video Things (IoVT) and **privacy-preserving edge AI**
- Shivaram Venkataraman (University of Wisconsin-Madison, USA) - real-time video processing
- Andrea Cavallaro (Queen Mary University of London, UK) - multi-modal fusion, **privacy-aware video analytics (based on adversarial-training/learning)**
- Amit K. Roy-Chowdhury (UC Riverside, USA) - tracking, reID, super-resolution and domain adaptation
- Jenq-Neng Hwang (University of Washington, USA) - tracking, reID, localization and visual odometry
- Hamed Haddadi (Imperial College London, UK) - **privacy-preserving edge AI**
- Ying Wu (Northwestern, USA) - tracking, detection, reID and segmentation
- Gaoang Wang (Zhejiang University, China) - scene-aware multi-object tracking
- Haibin Ling (Stony Brook University, USA) - visual tracking in drones
- Mubarak Shah (University of Central Florida, USA) - zero/few-shot learning in video based tracking/segmentation/action recognition
- Ming-Hsuan Yang (UC Merced, USA) - low-resources (data or compute) learning for tracking/detection/segmentation
- Fengyuan Xu (Nanjing University, China) - the Internet of Video Things (IoVT) and **privacy-preserving edge AI**
- Arun Kumar (University of California San Diego, USA) - data management and deep learning based data analytics
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Workshops (video analytics)
- The 3rd Workshop on Hot Topics in Video Analytics and Intelligent Edges (*ACM MobiCom'21*) - focus on deep learning based video analytics
- Multi-camera Multiple People Tracking Workshop (*IEEE ICCV'21*) - track multiple people from indoor scenes using multiple RGB cameras
- Multimedia Systems Conference (*ACM MMSys'21*) - contain multiple topics in video analysis
- Video Stream Analytics Reading List (Vrije Universiteit Amsterdam) - general, edge-cloud hybrid, edge-based, cloud-based, privacy and camera virtualization.
- Literature of video streaming research (Stony Brook) - video streaming -->
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Courses
- 706.550: Architecture of ML Systems (Summer 2021, Graz University of Technology) - the architecture and essential concepts of modern ML systems for both local and large-scale machine learning (based on non-deep ML analytics)
- CS231A: Computer Vision, From 3D Reconstruction to Recognition (Winter 2021, Stanford) - focus on basic concepts behind many computer vision tasks across multi-camera networks (camera models, calibration, single- and multiple-view geometry, stereo systems, sfm, stereo, matching, depth estimation, optical flow and optimal estimation)
- COS 598a: Machine Learning-Driven Video Systems (Spring 2022, Princeton) - target to recent research interests on video analytics (_Strong Recommendation_)
- CS34702 Topics in Networks: Machine Learning for Networking and Systems (Fall 2020, UChicago) - target to awesome recent research works on netwoking system (_video streaming and cloud scheduing_ are recommended)
- CSE 234: Data Systems for Machine Learning (Fall 2021, UCSD) - focus on the lifecycle of ML-based data analytics, including data sourcing and preparation for ML, programming models and systems for scalable ML model building, and systems for faster ML deployment
- CSE 291F: Advanced Data Analytics and ML Systems (Winter 2019, UCSD) - the emerging area of advanced data analytics and ML systems, at the intersection of data management, ML/AI, and systems.
- CS6465: Emerging Cloud Technologies and Systems Challenges (Fall 2019, Cornell) - emerging cloud computing technology, opportunities and challenges.
- CS294: Machine Learning Systems (Fall 2019, Berkeley) - contain all concepts/background behind machine learning systems (the best reference website!)
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Topics
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System
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- 3 - target to solve when to retrain models and how to reduce resource usage for multi-tasks (many inference and retraining tasks). <br>
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- 1 - target to detect domain drift and update corresponding models automatically. <br>
- 2 - the first model-less prediction serving system <br>
- 3 - model selection based on the automatically detected class skews<br>
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- 9 - expect to improve prediction serving's performance via ensembling learning<br>
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- Tutorial on privacy-preserving data analysis (The Alan Turing Institute)
- The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)
- A Dive into Privacy Preserving Machine Learning (OpML'20)
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- 6 - scheduling multiple DL jobs in resource-constrainted devices<br>
- 5 - consider input complexity <br>
- 4 - identify and harvest idle resources<br>
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AI Algorithm
- 1 - nyu/Awesome-Multi-Camera-Network/blob/master/Automatic_Labeling.md)
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Dataset
Programming Languages