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Multi-Camera Networks

Multi-camera Networks research notes.
Inspired by book, I collect papers from four topics in research opportunities:

  1. Camera Calibration.
  2. AI Applications (surveilliance systems, multi-view collaboration, multi-camera collaboration, efficient object detection, automatic labeling, MTMC tracking).
  3. Video Compression (for efficient communication).
  4. Database (for fast indexing).

In the end, I list datasets and useful toolboxes.


  1. Multi-Camera Networks: Principles and Applications. 2005.
  2. Camera Networks: The Acquisition and Analysis of Videos over Wide Areas (Synthesis Lectures on Computer Vision). 2012.


[1] M.Valera et al. Intelligent distributed surveillance systems: a review. 2005.
[2] Wang et al. Intelligent multi-camera video surveillance: a review. 2012.
[3] Ye et al. Wireless Video Surveillance: A Survey. 2013.
[4] Zhang et al. Deep Learning in Mobile and Wireless Networking: A Survey. IEEE TRANS 2019.

Researchers and labs


  1. Ganesh Ananthanarayanan (MSR)
  2. Yuanchao Shu (MSR)
  3. Andrea Cavallaro (QMUL)
  4. Amit K. Roy-Chowdhury (UC Riverside)
  5. Jenq-Neng Hwang (UW)
  6. Hamid K. Aghajan (UGent)
  7. Umakishore Ramachandran (Gatech)


  1. Live Video Analytics (MSR)
  2. Information Processing Lab (Washington)
  3. Video Computing Group (UC Riverside)
  4. Vision Research Lab (UCSB)
  5. Audio-visual Signal processing and Communication Systems (Berkeley)

Research opportunities

Camera calibration

[1] Calibration Wizard: A Guidance System for Camera Calibration Based on Modeling Geometric and Corner Uncertainty. In ICCV'19.

AI applications (todo)

Surveilliance systems (reducing deployment cost)

[1] Zhang et al. The Design and Implementation of a Wireless Video Surveillance System. In MobiCom'15.
[2] Jain et al. Scaling Video Analytics Systems to Large Camera Deployments. In HotMobile'19.
[3] Xu et al. Approximate Query Service on Autonomous IoT Cameras. In MobiSys'20.
[4] Bhardwaj et al. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers. In arXiv. - target to solve when to retrain models and how to reduce resource usage for multi-tasks (many inference and retraining tasks).

Multi-View Collaboration (epipolar geometry)

[1] Kocabas et al. Self-Supervised Learning of 3D Human Pose using Multi-view Geometry. In CVPR'19.
[2] Yao et al. MONET: Multiview Semi-supervised Keypoint Detection via Epipolar Divergence. In ICCV'19.
[3] Qiu et al. Cross View Fusion for 3D Human Pose Estimation. In ICCV'19.
[4] Brickwedde et al. Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes. In ICCV'19.
[5] Trinidad et al. Multi-view Image Fusion. In ICCV'19.
[6] Nassar et al. Simultaneous multi-view instance detection with learned geometric soft-constraints. In ICCV'19.
[7] Yao et al. Multiview Cross-supervision for Semantic Segmentation. In WACV'20.
[8] Zhang et al. Multiview Supervision By Registration. In WACV'20.

Multi-Camera Collaboration (exploring collaboration in a large camera networks, such as drone networks)

[1] Liu et al. Who2com: Collaborative Perception via Learnable Handshake Communication. In ICRA'20.
[2] Liu et al. When2com: Multi-Agent Perception via Communication Graph Grouping. In CVPR'20.

Efficient Object Detection (popular in autonomous cars or surveilliance cameras)

[1] Jiwoong et al. Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving. In ICCV'19. Public Code Note
[2] Wang et al. Learning Rich Features at High-Speed for Single-Shot Object Detection. ICCV'19. Public Code Note

Automatic Labeling (object detection and reID)

[1] H. Aghdam et al. Active Learning for Deep Detection Neural Networks. In ICCV'19. Public Code Note

MTMC tracking (todo)


[1] Qiu et al. Kestrel: Video Analytics for Augmented Multi-Camera Vehicle Tracking. In IOTDI'18.
[2] Xu et al. STTR: A System for Tracking All Vehicles All the Time At the Edge of the Network. In DEBS'18.
[3] Gupta et al. FogStore: A Geo-Distributed Key-Value Store Guaranteeing Low Latency for Strongly Consistent Access. In DEBS'18.
[4] Hung et al. Wide-area Analytics with Multiple Resources. In EuroSys'18.
[5] Jiang et al. Networked Cameras Are the New Big Data Clusters. In MobiCom’19 workshop.
[6] Emmons et al. Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary. In MobiCom’19 workshop.
[7] Xu et al. Space-Time Vehicle Tracking at the Edge of the Network. In MobiCom’19 workshop. [8] Xu et al. Coral-Pie: A Geo-Distributed Edge-compute Solution for Space-Time Vehicle Tracking. In Middleware'20.

Algorithms (MTMC Tracking)

[1] Yu et al. The Solution Path Algorithm for Identity-Aware Multi-Object Tracking. In CVPR'16.
[2] Ristani et al. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR'18.
[3] Feng et al. Challenges on Large Scale Surveillance Video Analysis. In CVPR'18 workshop.

Algorithms (collaborative learning between reID and detection)

[1] Gidaris et al. LocNet: Improving Localization Accuracy for Object Detection. In CVPR'16.
[2] Li et al. Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation. In ECCV'18.
[3] Huang et al. Adversarially Occluded Samples for Person Re-identification. In CVPR'18.
[4] Wang et al. Resource Aware Person Re-identification across Multiple Resolutions. In CVPR'18.
[5] Gong et al. Improving Multi-stage Object Detection via Iterative Proposal Refinement. In BMVC'19.
[6] Luo et al. Detect or Track: Towards Cost-Effective Video Object Detection/Tracking. In AAAI'19.
[7] He et al. Bounding Box Regression with Uncertainty for Accurate Object Detection. In CVPR'19.
[8] Qi et al. A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification. In ICCV'19.
[9] Zhu et al. Intra-Camera Supervised Person Re-Identification: A New Benchmark. In ICCV'19 workshop.
[10] Wang et al. Exploit the Connectivity: Multi-Object Tracking with TrackletNet. In MM'19.

Video compression

[1] Naderiparizi et al. Towards Battery-Free HD Video Streaming. In NSDI’18.
[2] Baig et al. Jigsaw: Robust Live 4K Video Streaming. In MobiCom'19.
[3] Rippel et al. Learned Video Compression. In ICCV 2019.
[4] Djelouah et al. Neural Inter-Frame Compression for Video Coding. In ICCV 2019.
[5] Habibian et al. Video Compression with Rate-Distortion Autoencoders. In ICCV 2019.
[6] Xu et al. Non-Local ConvLSTM for Video Compression Artifact Reduction. In ICCV 2019.


[1] Saurez et al. A drop-in middleware for serializable DB clustering across geo-distributed sites. In VLDB'20.


  1. Duke MTMC (8 cameras, non-overlapping)
  2. Nvidia CityFlow (>40 cameras, overlapping and non-overlapping)
  3. EPFL WildTrack (7 cameras, overlapping)
  4. EPFL-RLC (3 cameras, overlapping)
  5. CMU Panoptic Dataset (>50 cameras, overlapping)
  6. University of Illinois STREETS (100 cameras, non-overlapping)
  7. Awesome reID dataset


  1. CHUK-mmcv: a foundational python library for computer vision research and supports many research projects (2D/3D detection, semantic segmentation, image and video editing, pose estimation, action understanding and image classification).
  2. JDCV-fastreid: a python library implementing SOTA re-identification methods (including pedestrian and vehicle re-identification). They also provided a good documentation.


Multi-camera Network research resources




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