Approximate Query Processing on Autonomous Cameras
暂无分享,去创建一个
Xuanzhe Liu | Yunxin Liu | Xiwen Zhang | Felix Xiaozhu Lin | Mengwei Xu | Xuanzhe Liu | Yunxin Liu | F. Lin | Mengwei Xu | Xiwen Zhang
[1] Sitanshu Sekhar Kar,et al. IS 30 THE MAGIC NUMBER? ISSUES IN SAMPLE SIZE ESTIMATION , 2013 .
[2] Peder A. Olsen,et al. Crowd Counting with Decomposed Uncertainty , 2019, AAAI.
[3] Felix Xiaozhu Lin,et al. VStore: A Data Store for Analytics on Large Videos , 2018, EuroSys.
[4] Sridhar Ramaswamy,et al. The Aqua approximate query answering system , 1999, SIGMOD '99.
[5] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[6] Fei Liu,et al. A video-based real-time adaptive vehicle-counting system for urban roads , 2017, PloS one.
[7] Boby George,et al. A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic , 2012, IEEE Transactions on Instrumentation and Measurement.
[8] Chris Jermaine,et al. Online aggregation for large MapReduce jobs , 2011, Proc. VLDB Endow..
[9] Ranveer Chandra,et al. FarmBeats: An IoT Platform for Data-Driven Agriculture , 2017, NSDI.
[10] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[11] Samvit Jain,et al. ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale , 2018, ArXiv.
[12] Ion Stoica,et al. BlinkDB: queries with bounded errors and bounded response times on very large data , 2012, EuroSys '13.
[13] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[14] Paramvir Bahl,et al. Live Video Analytics at Scale with Approximation and Delay-Tolerance , 2017, NSDI.
[15] Thomas H. Clausen,et al. A Study of LoRa: Long Range & Low Power Networks for the Internet of Things , 2016, Sensors.
[16] W. J. Dixon,et al. Introduction to Statistical Analysis , 1951 .
[17] B GibbonsPhillip,et al. The Aqua approximate query answering system , 1999 .
[18] Winston H. Hsu,et al. Drone-Based Object Counting by Spatially Regularized Regional Proposal Network , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[19] Shan Lu,et al. ALERT: Accurate Anytime Learning for Energy and Timeliness , 2019, ArXiv.
[20] Jay Kreps,et al. Kafka : a Distributed Messaging System for Log Processing , 2011 .
[21] Thu D. Nguyen,et al. Designing and Managing Data centers Powered by Renewable Energy , 2014, IEEE Micro.
[22] Ion Stoica,et al. Chameleon: scalable adaptation of video analytics , 2018, SIGCOMM.
[23] Wilfrid J. Dixon,et al. An Introduction to Statistical Analysis , 1951 .
[24] Viswanath Poosala,et al. Congressional samples for approximate answering of group-by queries , 2000, SIGMOD '00.
[25] Pat Hanrahan,et al. Scanner: Efficient Video Analysis at Scale , 2018, ACM Trans. Graph..
[26] Jean-Jacques Chaillout,et al. Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN , 2018, Sensors.
[27] Jarrod C Hodgson,et al. Precision wildlife monitoring using unmanned aerial vehicles , 2016, Scientific Reports.
[28] Vldb Endowment,et al. The VLDB journal : the international journal on very large data bases. , 1992 .
[29] Ameet Talwalkar,et al. Knowing when you're wrong: building fast and reliable approximate query processing systems , 2014, SIGMOD Conference.
[30] Yong Wang,et al. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.
[31] Gaurav Sharma,et al. UR-SolarCap: An Open Source Intelligent Auto-Wakeup Solar Energy Harvesting System for Supercapacitor-Based Energy Buffering , 2016, IEEE Access.
[32] Robert Ricci,et al. Taming Performance Variability , 2018, OSDI.
[33] Camillo J. Taylor,et al. Road Intersection Monitoring from Video with Large Perspective Deformation , 2014 .
[34] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Matei Zaharia,et al. NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale , 2017, Proc. VLDB Endow..
[36] Surajit Chaudhuri,et al. Dynamic sample selection for approximate query processing , 2003, SIGMOD '03.
[37] Chengyi Wang,et al. An Automatic Car Counting System Using OverFeat Framework , 2017, Sensors.
[38] Ana Garrido-Varo,et al. A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd , 2019, Sensors.
[39] Alanson P. Sample,et al. Self-localizing battery-free cameras , 2015, UbiComp.
[40] Pascal Mettes,et al. Nature Conservation Drones for Automatic Localization and Counting of Animals , 2014, ECCV Workshops.
[41] Jordi Torres,et al. GreenHadoop: leveraging green energy in data-processing frameworks , 2012, EuroSys '12.
[42] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Zheng Zhang,et al. Error-bounded Sampling for Analytics on Big Sparse Data , 2014, Proc. VLDB Endow..
[44] Joshua R. Smith,et al. WISPCam: A battery-free RFID camera , 2015, 2015 IEEE International Conference on RFID (RFID).
[45] Joseph M. Hellerstein,et al. MapReduce Online , 2010, NSDI.
[46] Michael Maire,et al. ALERT: Accurate Learning for Energy and Timeliness , 2019, USENIX Annual Technical Conference.
[47] Joseph E. Gonzalez,et al. ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale , 2018 .
[48] Reuven Y. Rubinstein,et al. Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.
[49] David C. Anastasiu,et al. The NVIDIA AI City Challenge , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).
[50] Kyuseok Shim,et al. Approximate query processing using wavelets , 2001, The VLDB Journal.
[51] Hyeontaek Lim,et al. Scaling Video Analytics on Constrained Edge Nodes , 2019, MLSys.
[52] Katia Obraczka,et al. Solar-powered, wireless smart camera network: An IoT solution for outdoor video monitoring , 2018, Comput. Commun..
[53] Brandon Lucia,et al. A Reconfigurable Energy Storage Architecture for Energy-harvesting Devices , 2018, ASPLOS.
[54] Xiaofan Jiang,et al. City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras , 2019, UbiComp/ISWC Adjunct.
[55] Jitendra Malik,et al. A real-time computer vision system for measuring traffic parameters , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[56] Joshua R. Smith,et al. Towards Battery-Free HD Video Streaming , 2018, NSDI.
[57] Taro Yamane. Statistics: An Introductory Analysis , 1965 .
[58] Weisong Shi,et al. LAVEA: latency-aware video analytics on edge computing platform , 2017, SEC.
[59] Peng Liu,et al. EdgeEye: An Edge Service Framework for Real-time Intelligent Video Analytics , 2018, EdgeSys@MobiSys.
[60] Surajit Chaudhuri,et al. Optimized stratified sampling for approximate query processing , 2007, TODS.
[61] Shenghua Gao,et al. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[62] F. Massey,et al. Introduction to Statistical Analysis , 1970 .
[63] Martin L. Kersten,et al. SciBORQ: Scientific data management with Bounds On Runtime and Quality , 2011, CIDR.
[64] Lin Zhong,et al. RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).
[65] P. Karpagavalli,et al. Estimating the density of the people and counting the number of people in a crowd environment for human safety , 2013, 2013 International Conference on Communication and Signal Processing.
[66] Shree K. Nayar,et al. Towards Self-Powered Cameras , 2015, 2015 IEEE International Conference on Computational Photography (ICCP).
[67] Touradj Ebrahimi,et al. A framework for the validation of privacy protection solutions in video surveillance , 2010, 2010 IEEE International Conference on Multimedia and Expo.
[68] Dirk P. Kroese,et al. Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.
[69] Yishay Mansour,et al. Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.
[70] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[71] Zhenming Liu,et al. DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.
[72] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[73] Peter Bailis,et al. BlazeIt: Fast Exploratory Video Queries using Neural Networks , 2018, ArXiv.
[74] Helen J. Wang,et al. Online aggregation , 1997, SIGMOD '97.
[75] Hao Yu,et al. An Ultra-Low Power CMOS Image Sensor with On-Chip Energy Harvesting and Power Management Capability , 2015, Sensors.
[76] Tanya Y. Berger-Wolf,et al. Animal Population Censusing at Scale with Citizen Science and Photographic Identification , 2017, AAAI Spring Symposia.
[77] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[78] Margaret Kosmala,et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning , 2017, Proceedings of the National Academy of Sciences.
[79] Prashant J. Shenoy,et al. Yank: Enabling Green Data Centers to Pull the Plug , 2013, NSDI.
[80] Brandon Lucia,et al. An Energy-interference-free Hardware-Software Debugger for Intermittent Energy-harvesting Systems , 2016, ASPLOS.
[81] Xuanzhe Liu,et al. Supporting Video Queries on Zero-Streaming Cameras , 2019, ArXiv.
[82] Gaurav Sharma,et al. Hybrid Solar-Wind Energy Harvesting for Embedded Applications: Supercapacitor-Based System Architectures and Design Tradeoffs , 2017, IEEE Circuits and Systems Magazine.
[83] Brandon Lucia,et al. Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems , 2018, ASPLOS.
[84] Ryan J. Halter,et al. Amulet: An Energy-Efficient, Multi-Application Wearable Platform , 2016, SenSys.
[85] Honghui Shi,et al. Geometry-Aware Traffic Flow Analysis by Detection and Tracking , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[86] Minos N. Garofalakis,et al. Approximate Query Processing: Taming the TeraBytes , 2001, VLDB.
[87] B. Yogameena,et al. Computer vision based crowd disaster avoidance system: A survey , 2017 .
[88] Paramvir Bahl,et al. Focus: Querying Large Video Datasets with Low Latency and Low Cost , 2018, OSDI.
[89] Joshua R. Smith,et al. Battery-Free Connected Machine Vision with WISPCam , 2016, GETMBL.
[90] Thomas Watteyne,et al. Understanding the Limits of LoRaWAN , 2016, IEEE Communications Magazine.
[91] Xuanzhe Liu,et al. Querying Zero-streaming Cameras , 2019 .
[92] Nitin Agrawal,et al. Low-Latency Analytics on Colossal Data Streams with SummaryStore , 2017, SOSP.
[93] Zhuo Chen,et al. Bandwidth-Efficient Live Video Analytics for Drones Via Edge Computing , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).
[94] Xu Liu,et al. Highway Vehicle Counting in Compressed Domain , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[95] Mahadev Satyanarayanan,et al. A Scalable and Privacy-Aware IoT Service for Live Video Analytics , 2017, MMSys.