# Index

### System

* [**Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices**](http://people.csail.mit.edu/yuhan/doc/sen060-chenA.pdf) - Chen et al., SenSys '15
  * Cache the results to hide the network delivery and server processing latency
  * Only send the frames that are largely different from the previous frames
* [**Starfish: Efficient Concurrency Support for Computer Vision Applications**](https://dl.acm.org/doi/pdf/10.1145/2742647.2742663) - LiKamWa et al., MobiSys '15
  * Track identical library calls and reuse computed results across multiple applications
* [**The Design and Implementation of a Wireless Video Surveillance System**](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/08/Bahl-MobiCom-2015.pdf) - Zhang et al., MobiCom '15 
  * Group cameras monitoring the same area into clusters
  * Uploads frames with high “utility”(e.g., object count)
  * Uploads frames that are different from previous frames(e.g., different object counts)
* [**MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints**](https://homes.cs.washington.edu/~arvind/papers/mcdnn.pdf)  - Han et al., MobiSys '16
  * Adaptively pick the best specialized model 
* [**Optasia: A Relational Platform for Efficient Large-Scale Video Analytics**](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/optasia_socc16.pdf) -  Lu et al., SOCC '16
* [**DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware**](https://dl.acm.org/doi/10.1145/3081333.3081359) **-** Mathut et al., MobiSys '17
  * A system that can run multiple cloud-scale DL models locally on wearable devices
  * Interleave the loading of memory-intensive FC layers and the execution of compute-intensive convolution layers
* [**Fast Video Classification via Adaptive Cascading of Deep Models**](http://openaccess.thecvf.com/content_cvpr_2017/papers/Shen_Fast_Video_Classification_CVPR_2017_paper.pdf) - Shen et al., CVPR '17
  * Leverage the short-term class skew using model cascade
  * Train specialized video online
* [**NoScope: Optimizing Neural Network Queries over Video at Scale**](https://arxiv.org/abs/1703.02529)  - Kang et al., VLDB '17
  * Model cascade: difference detector(MSE) → cheap/specialized model → full model
* [**Live Video Analytics at Scale with Approximation and Delay-Tolerance**](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-zhang.pdf) - Zhang et al., NSDI '17
  * Objective: support efficient real-time analytics for multiple queries which have different quality and lag goals
  * Offline Phase: use profiler to get a set of pareto-optimal configurations(a combination of knobs) from resource-quality space (with a variant of greedy hill-climbing)
  * Online Phase: periodically change running queries’ configurations/placement/resource allocation to maximize total utility(quality + lag goals)
* [**Cachier: Edge-Caching for Recognition Applications**](https://ieeexplore.ieee.org/document/7979974) - Drolia et al., ICDCS '17
  * Use edge server as a cache with compute resources(similar to CDN)
* [**LAVEA: Latency-aware Video Analytics on Edge Computing Platform**](http://www.cs.wm.edu/~syi/publications/sedgec17_1.pdf) - Yi et al., SEC '17
* [**Neurosurgeon: collaborative intelligence between the cloud and the mobile edge**](https://web.eecs.umich.edu/~jahausw/publications/kang2017neurosurgeon.pdf) - Kang et al., ASPLOS '17 \[[Morning Paper Summary](https://blog.acolyer.org/2017/05/25/neurosurgeon-collaborative-intelligence-between-the-cloud-and-the-mobile-edge/)]
  * Observed that 1) data transfer latency is often higher than mobile computation latency, especially on wireless networks. 2) inside a model, data size is decreasing at the front-end whereas per-layer latency is higher at the back-end.
  * NOTE: 2) isn't necessarily true for recent networks with global average pooling 
* [**Scanner: Efficient Video Analysis at Scale**](https://arxiv.org/abs/1805.07339) - Poms et al., SIGGRAPH '18
  * Store videos as tables which are optimized for frame sampling on compressed videos
  * Express frame operations as dataflow graphs
* [**Mainstream: Dynamic Stem-Sharing for Multi-Tenant Video Processing**](https://www.usenix.org/system/files/conference/atc18/atc18-jiang.pdf) - Jiang et al., ATC '18
  * Transfer learning → execute common layers only once
  * Processing more frames with shared DNN vs. greater per-frame accuracy with specialized DNN
* [**Chameleon: Scalable Adaptation of Video Analytics**](https://people.cs.uchicago.edu/~junchenj/docs/Chameleon_SIGCOMM_CameraReady_faceblurred.pdf) - Jiang et al., SIGCOMM '18
  * Resource-accuracy tradeoff is affected by some persistent characteristics, so we can reuse configurations over time → temporal correlation
  * Video cameras with the same characteristics share the same best configurations → cross-camera correlations
  * Configuration knobs independently impact accuracy → reduce search space
  * Divide cameras into groups → periodically re-profile “leader” videos 
* [**AWStream: Adaptive Wide-Area Streaming Analytics**](https://awstream.github.io/paper/awstream.pdf) - Zhang et al., SIGCOMM '18
  * Objective: low latency and high accuracy stream processing in WAN
  * Ask programmers to write degradation functions and profile those configurations
  * Adaptively change the configuration at runtime → react to congestions
* [**Focus: Querying Large Video Datasets with Low Latency and Low Cost**](https://www.usenix.org/conference/osdi18/presentation/hsieh) - Hsieh et al., OSDI '18
  * Enable low-latency and low-cost querying over large historical video datasets.
  * At ingest time: classify objects using a cheap CNN, cluster similar objects(KNN search), and index each cluster using top-K most confident classification results.
  * At query-time: looks up the ingest index for cluster centroids that match the class and classifies them using expensive CNN. 
* [**On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework**](https://tik-old.ee.ethz.ch/file/79a7dd6f6370f809e6180c0746232283/mobisys18-liu.pdf) - Liu et al., MobiSys '18
  * Adaptively select DNN compression techniques based on user demand(Acc/Storage/Comp cost/Latency/Energy) 
* [**Sprocket: A Serverless Video Processing Framework**](http://cseweb.ucsd.edu/~gmporter/papers/socc18-sprocket.pdf) - Ao et al., SoCC '18
  * Extend the idea of [ExCamera](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-fouladi.pdf) - enable users to build more complex pipelines
  * novel straggler mitigation strategy 
* [**Potluck: Cross-Application Approximate Deduplication for Computation-Intensive Mobile Applications**](https://www.cs.yale.edu/homes/guo-peizhen/files/potluck-asplos18.pdf) - Guo et al., ASPLOS '18
* [**ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale**](https://arxiv.org/abs/1811.01268) - Jain et al., arXiv' 18
* [**DeepLens: Towards a Visual Data Management System**](http://cidrdb.org/cidr2019/papers/p40-krishnan-cidr19.pdf) - Krishnan et al., CIDR '19
  * Objective: Indexing and query optimization for VDMS(For complex queries like join)
  * A novel model for encoding, indexing and storing lineage
* [**VStore: A Data Store for Analytics on Large Videos**](https://web.ics.purdue.edu/~xu944/eurosys19.pdf) - Xu et al., EuroSys '19
* [**Networked Cameras Are the New Big Data Clusters**](https://www.microsoft.com/en-us/research/uploads/prod/2019/08/hotedgevideo19camera.pdf) - Jiang et al., HotEdgeVideo '19
  * Proposes a new “camera cluster” abstraction
    * Saving computing resource
    * Resource Pooling
    * Improving analytics quality 
    * Hiding low-level intricacies
* [**Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary**](https://dl.acm.org/doi/abs/10.1145/3349614.3356023) - Emmons et al., HotEdgeVideo '19
  * Split-brain inference
* [**Scaling Video Analytics on Constrained Edge Nodes**](https://arxiv.org/abs/1905.13536) - Canel et al., SysML '19
  * Assumption: relevant events are rare.
  * Filter frames by using a micro, binary classifier that extract feature maps from base DNN
* [**AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling**](https://arxiv.org/pdf/1902.02910.pdf) - Chin et al., SysML '19
  * Down-sampling images are sometimes beneficial in terms of accuracy(e.g., removing background noise)
  * Adaptively scaling video to improve both speed and accuracy of object detectors
* [**Bridging the Edge-Cloud Barrier for Real-time Advanced Vision Analytics**](https://www.usenix.org/conference/hotcloud19/presentation/wang) - Wang et al., HotCloud 19
  * Use super-resolution to enhance video quality before running analytics(related: [NAS](https://www.usenix.org/system/files/osdi18-yeo.pdf))
* [**Edge Assisted Real-time Object Detection for Mobile Augmented Reality**](http://www.winlab.rutgers.edu/~luyang/papers/mobicom19_augmented_reality.pdf) - Liu et al., MobiCom '19
  * Dynamic RoI Encoding: decrease the encoding quality of uninterested areas(use the last processed frame as heuristic)
  * (Dependency-aware) Parallel streaming and inference: divide frames into slices and parallelize the processing between slices
* [**Scaling Video Analytics Systems to Large Camera Deployments**](https://rtcl.eecs.umich.edu/yuanchao/paper/hotmobile19video.pdf) - Jain et al., HotMobile '19
  * Leverage cross-camera correlations to reduce resource usage and achieve higher inference accuracy
* [**Visual Road: A Video Data Management Benchmark**](https://db.cs.washington.edu/projects/visualroad/p300-haynes.pdf) - Haynes et al., SIGMOD '19
  * An auto-generated benchmark that evaluates the performance of VDBMS
  * Let users place an arbitrary number of cameras, each with configurable position, resolution, and field of view
  * Composite queries and automatically generated ground truth labels
* [**Rekall: Specifying Video Events using Compositions of Spatiotemporal Labels**](https://arxiv.org/abs/1910.02993) - Fu et al., arXiv '19
* [**BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics**](https://cs.stanford.edu/~matei/papers/2020/vldb_blazeit.pdf) - Kang et al., VLDB '20
  * Objective: Support (approximate) aggregate and limit queries over large video dataset
  * At ingest time, run object detection on small samples of frames and store them
  * For each query, use them to train a query-specific proxy model
* [**MIRIS: Fast Object Track Queries in Video** ](https://favyen.com/miris-sigmod.pdf)- Bastani et al., SIGMOD '20
* [**Server-Driven Video Streaming for Deep Learning Inference**](https://dl.acm.org/doi/pdf/10.1145/3387514.3405887) - Du et al., SIGCOMM '20
  * Iterative video processing driven by server-side DNN
* [**Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics**](https://dl.acm.org/doi/pdf/10.1145/3387514.3405874) - Li et al., SIGCOMM '20
  * Dynamically adapts filtering decisions based on feature type, threshold, etc.
* [**Visor: Privacy-Preserving Video Analytics as a Cloud Service**](https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Visor-Privacy-Preserving-Video-Analytics-as-a-Cloud-Service.pdf) - Poddar - et al., Security '20
* [**Panorama: A Data System for Unbounded Vocabulary Querying over Video**](http://www.vldb.org/pvldb/vol13/p477-zhang.pdf) - Zhang et al., VLDB '20
  * A system that let users generalize to unbounded vocabularies without manual retraining
* [**Real-Time Video Inference on Edge Devices via Adaptive Model Streaming**](https://arxiv.org/pdf/2006.06628.pdf) - Khani et al., arXiv '20

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