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1. Spatula: Efficient cross-camera video analytics on large camera networks

   https://doi.org/10.1109/SEC50012.2020.00016  

   Jain, Samvit; Zhang, Xun; Zhou, Yuhao; Ananthanarayanan, Ganesh; Jiang, Junchen; Shu, Yuanchao; Bahl, Paramvir; Gonzalez, Joseph (November 2020, 2020 IEEE/ACM Symposium on Edge Computing (SEC))

   null (Ed.)

   Cameras are deployed at scale with the purpose of searching and tracking objects of interest (e.g., a suspected person) through the camera network on live videos. Such cross-camera analytics is data and compute intensive, whose costs grow with the number of cameras and time. We present Spatula, a cost-efficient system that enables scaling cross-camera analytics on edge compute boxes to large camera networks by leveraging the spatial and temporal cross-camera correlations. While such correlations have been used in computer vision community, Spatula uses them to drastically reduce the communication and computation costs by pruning search space of a query identity (e.g., ignoring frames not correlated with the query identity’s current position). Spatula provides the first system substrate on which cross-camera analytics applications can be built to efficiently harness the cross-camera correlations that are abundant in large camera deployments. Spatula reduces compute load by $$8.3\times$$ on an 8-camera dataset, and by $$23\times-86\times$$ on two datasets with hundreds of cameras (simulated from real vehicle/pedestrian traces). We have also implemented Spatula on a testbed of 5 AWS DeepLens cameras. 

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2. Improving Multi-Camera View Recommendation with Temporal and Camera Embedding

   https://doi.org/10.1109/IE64880.2025.11130132  

   Lee, Kuan-Ying; Zhou, Qian; Nahrstedt, Klara (June 2025, IEEE)

   Multi-camera systems are essential in movies, live broadcasts, and other media. The selection of the appropriate camera for every moment has a decisive impact on production quality and audience preferences. Learning-based multi-camera view recommendation frameworks have been explored to assist professionals in decision making. This work explores how two standard cinematography practices could be incorporated into the learning pipeline: (1) not staying on the same camera for too long and (2) introducing a scene from a wider shot and gradually progressing to narrower ones. In these regards, we incorporate (1) the duration of the displaying camera and (2) camera identity as temporal and camera embedding in a transformer architecture, thereby implicitly guiding the model to learn the two practices from professional-labeled data. Experiments show that the proposed framework outperforms the baseline by 14.68% in six-way classification accuracy. Ablation studies on different approaches to embedding the temporal and camera information further verify the efficacy of the framework. 

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3. Towards Accurate Active Camera Localization

   Qihang Fang, Yingda Yin (October 2022, European Conference on Computer Vision 2022)

   In this work, we tackle the problem of active camera localization, which controls the camera movements actively to achieve an accurate camera pose. The past solutions are mostly based on Markov Localization, which reduces the position-wise camera uncertainty for localization. These approaches localize the camera in the discrete pose space and are agnostic to the localization-driven scene property, which restricts the camera pose accuracy in the coarse scale. We propose to overcome these limitations via a novel active camera localization algorithm, composed of a passive and an active localization module. The former optimizes the camera pose in the continuous pose space by establishing point-wise camera-world correspondences. The latter explicitly models the scene and camera uncertainty components to plan the right path for accurate camera pose estimation. We validate our algorithm on the challenging localization scenarios from both synthetic and scanned real-world indoor scenes. Experimental results demonstrate that our algorithm outperforms both the state-of-the-art Markov Localization based approach and other compared approaches on the fine-scale camera pose accuracy 

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4. Plasmonic Computational Compound-Eye Camera

   https://doi.org/10.1364/OPN.31.12.000041  

   Kogos, Leonard; Li, Yunzhe; Liu, Jianing; Li, Yuyu; Tian, Lei; Paiella, Roberto (January 2020, Optics and Photonics News)

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5. The SPICEcore Hole Camera System

   https://doi.org/10.22323/1.358.0926  

   Dujmovi\'c, Hrvoje; Jeong, Minjin; Tönnis, Christoph (January 2020, Pos proceedings of science)