The Experts below are selected from a list of 3594 Experts worldwide ranked by ideXlab platform
Arun Hampapur - One of the best experts on this subject based on the ideXlab platform.
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Video Analytics for surveillance theory and practice from the guest editors
IEEE Signal Processing Magazine, 2010Co-Authors: Carlo S. Regazzoni, Andrea Cavallaro, Ying Wu, Janusz Konrad, Arun HampapurAbstract:Video Analytics, loosely defined as autonomous understanding of events occurring in a scene monitored by multiple Video cameras, has been rapidly evolving in the last two decades. Despite this effort, practical surveillance systems deployed today are not yet capable of autonomous analysis of complex events in the field of view of cameras. This is a serious deficiency as Video feeds from millions of surveillance cameras worldwide are not analyzed in real time and thus cannot help with accident, crime or terrorism prevention, and mitigation, issues critical to the contemporary society. Today, these feeds are, at best, recorded to facilitate post-event Video forensics.
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AVSS - Video Analytics in Urban Environments
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009Co-Authors: Arun Hampapur, Russell Bobbitt, Rick Kjeldsen, Mike Desimone, Carl Mercier, Chris Milite, Rogerio Feris, Lisa M. Brown, Stephen RussoAbstract:Urban environments present unique challenges from the perspective of surveillance and security. Threat activity in urban environments tends to be very similar to background activity, while the volume of activity is often very high. The widespread geographical area presents issues from the perspective of response. These characteristics of urban environments create challenges to traditional applications of Video Analytics technologies and opens up opportunities for novel approaches. This paper explores the applicability of Video Analytics in various scenarios presented in urban surveillance situations. We also describe novel technical solutions to some of the challenges of urban surveillance.
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Video Analytics in urban environments
6th IEEE International Conference on Advanced Video and Signal Based Surveillance AVSS 2009, 2009Co-Authors: Arun Hampapur, Russell Bobbitt, Rick Kjeldsen, Mike Desimone, Carl Mercier, Chris Milite, Rogerio Feris, Max Lu, Lisa Brown, Stephen RussoAbstract:Urban environments present unique challenges from the perspective of surveillance and security. Threat activity in urban environments tends to be very similar to background activity, while the volume of activity is often very high. The widespread geographical area presents issues from the perspective of response. These characteristics of urban environments create challenges to traditional applications of Video Analytics technologies and opens up opportunities for novel approaches. This paper explores the applicability of Video Analytics in various scenarios presented in urban surveillance situations. We also describe novel technical solutions to some of the challenges of urban surveillance.
Ganesh Ananthanarayanan - One of the best experts on this subject based on the ideXlab platform.
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Live Video Analytics
IEEE Internet Computing, 2019Co-Authors: Ganesh Ananthanarayanan, Weisong ShiAbstract:The articles in this special section focus on Video Analytics. According to a January 2013 report published by the National Public Radio, the Chinese government has installed more than 20 million cameras across the country. Similarly, a recent BBC report stated, there is one camera for every 14 people in London. Other large cities including New York, Paris, and Tokyo are also deploying cameras in large numbers, so it is reasonable to claim that networked cameras are everywhere. These cameras are deployed for a wide variety of commercial and security reasons. Further, consumer devices themselves have cameras with users interested in streaming Videos live from these devices.
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MobiSys - Video Analytics - Killer App for Edge Computing
Proceedings of the 17th Annual International Conference on Mobile Systems Applications and Services, 2019Co-Authors: Ganesh Ananthanarayanan, Victor Bahl, Landon P. Cox, Alex Crown, Shadi Nogbahi, Yuanchao ShuAbstract:The world is witnessing an unprecedented increase in camera deployment. The USA and UK, for instance, have one camera for every 8 people. Video Analytics from these cameras are becoming more and more pervasive, exerting important functions on a wide range of verticals including manufacturing, transportation, and retails. While vision techniques have seen considerable advancement, they have come at the expense of compute and network cost.
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HotMobile - Scaling Video Analytics Systems to Large Camera Deployments
Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications, 2019Co-Authors: Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Joseph E. GonzalezAbstract:Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying Video cameras en masse for the spatial monitoring of their physical premises. Scaling Video Analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale Video Analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that Video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera Video Analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling Video Analytics to large camera networks, and outline a plan for its realization.
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ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Xun Zhang, Yuhao Zhou, Joseph E. GonzalezAbstract:Enterprises are increasingly deploying large camera networks for Video Analytics. Many target applications entail a common problem template: searching for and tracking an object or activity of interest (e.g. a speeding vehicle, a break-in) through a large camera network in live Video. Such cross-camera Analytics is compute and data intensive, with cost growing with the number of cameras and time. To address this cost challenge, we present ReXCam, a new system for efficient cross-camera Video Analytics. ReXCam exploits spatial and temporal locality in the dynamics of real camera networks to guide its inference-time search for a query identity. In an offline profiling phase, ReXCam builds a cross-camera correlation model that encodes the locality observed in historical traffic patterns. At inference time, ReXCam applies this model to filter frames that are not spatially and temporally correlated with the query identity's current position. In the cases of occasional missed detections, ReXCam performs a fast-replay search on recently filtered Video frames, enabling gracefully recovery. Together, these techniques allow ReXCam to reduce compute workload by 8.3x on an 8-camera dataset, and by 23x - 38x on a simulated 130-camera dataset. ReXCam has been implemented and deployed on a testbed of 5 AWS DeepLens cameras.
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Scaling Video Analytics Systems to Large Camera Deployments
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Joseph E. GonzalezAbstract:Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying Video cameras en masse for the spatial monitoring of their physical premises. Scaling Video Analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale Video Analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that Video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera Video Analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling Video Analytics to large camera networks, and outline a plan for its realization.
Stephen Russo - One of the best experts on this subject based on the ideXlab platform.
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AVSS - Video Analytics in Urban Environments
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009Co-Authors: Arun Hampapur, Russell Bobbitt, Rick Kjeldsen, Mike Desimone, Carl Mercier, Chris Milite, Rogerio Feris, Lisa M. Brown, Stephen RussoAbstract:Urban environments present unique challenges from the perspective of surveillance and security. Threat activity in urban environments tends to be very similar to background activity, while the volume of activity is often very high. The widespread geographical area presents issues from the perspective of response. These characteristics of urban environments create challenges to traditional applications of Video Analytics technologies and opens up opportunities for novel approaches. This paper explores the applicability of Video Analytics in various scenarios presented in urban surveillance situations. We also describe novel technical solutions to some of the challenges of urban surveillance.
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Video Analytics in urban environments
6th IEEE International Conference on Advanced Video and Signal Based Surveillance AVSS 2009, 2009Co-Authors: Arun Hampapur, Russell Bobbitt, Rick Kjeldsen, Mike Desimone, Carl Mercier, Chris Milite, Rogerio Feris, Max Lu, Lisa Brown, Stephen RussoAbstract:Urban environments present unique challenges from the perspective of surveillance and security. Threat activity in urban environments tends to be very similar to background activity, while the volume of activity is often very high. The widespread geographical area presents issues from the perspective of response. These characteristics of urban environments create challenges to traditional applications of Video Analytics technologies and opens up opportunities for novel approaches. This paper explores the applicability of Video Analytics in various scenarios presented in urban surveillance situations. We also describe novel technical solutions to some of the challenges of urban surveillance.
Peter Bodik - One of the best experts on this subject based on the ideXlab platform.
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chameleon scalable adaptation of Video Analytics
ACM Special Interest Group on Data Communication, 2018Co-Authors: Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodik, Siddhartha Sen, Ion StoicaAbstract:Applying deep convolutional neural networks (NN) to Video data at scale poses a substantial systems challenge, as improving inference accuracy often requires a prohibitive cost in computational resources. While it is promising to balance resource and accuracy by selecting a suitable NN configuration (e.g., the resolution and frame rate of the input Video), one must also address the significant dynamics of the NN configuration's impact on Video Analytics accuracy. We present Chameleon, a controller that dynamically picks the best configurations for existing NN-based Video Analytics pipelines. The key challenge in Chameleon is that in theory, adapting configurations frequently can reduce resource consumption with little degradation in accuracy, but searching a large space of configurations periodically incurs an overwhelming resource overhead that negates the gains of adaptation. The insight behind Chameleon is that the underlying characteristics (e.g., the velocity and sizes of objects) that affect the best configuration have enough temporal and spatial correlation to allow the search cost to be amortized over time and across multiple Video feeds. For example, using the Video feeds of five traffic cameras, we demonstrate that compared to a baseline that picks a single optimal configuration offline, Chameleon can achieve 20-50% higher accuracy with the same amount of resources, or achieve the same accuracy with only 30--50% of the resources (a 2-3X speedup).
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SIGCOMM - Chameleon: scalable adaptation of Video Analytics
Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, 2018Co-Authors: Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodik, Siddhartha Sen, Ion StoicaAbstract:Applying deep convolutional neural networks (NN) to Video data at scale poses a substantial systems challenge, as improving inference accuracy often requires a prohibitive cost in computational resources. While it is promising to balance resource and accuracy by selecting a suitable NN configuration (e.g., the resolution and frame rate of the input Video), one must also address the significant dynamics of the NN configuration's impact on Video Analytics accuracy. We present Chameleon, a controller that dynamically picks the best configurations for existing NN-based Video Analytics pipelines. The key challenge in Chameleon is that in theory, adapting configurations frequently can reduce resource consumption with little degradation in accuracy, but searching a large space of configurations periodically incurs an overwhelming resource overhead that negates the gains of adaptation. The insight behind Chameleon is that the underlying characteristics (e.g., the velocity and sizes of objects) that affect the best configuration have enough temporal and spatial correlation to allow the search cost to be amortized over time and across multiple Video feeds. For example, using the Video feeds of five traffic cameras, we demonstrate that compared to a baseline that picks a single optimal configuration offline, Chameleon can achieve 20-50% higher accuracy with the same amount of resources, or achieve the same accuracy with only 30--50% of the resources (a 2-3X speedup).
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real time Video Analytics the killer app for edge computing
IEEE Computer, 2017Co-Authors: Ganesh Ananthanarayanan, Paramvir Bahl, Peter Bodik, Krishna Chintalapudi, Matthai Philipose, Lenin Ravindranath, Sudipta N SinhaAbstract:Video Analytics will drive a wide range of applications with great potential to impact society. A geographically distributed architecture of public clouds and edges that extend down to the cameras is the only feasible approach to meeting the strict real-time requirements of large-scale live Video Analytics.
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live Video Analytics at scale with approximation and delay tolerance
Networked Systems Design and Implementation, 2017Co-Authors: Haoyu Zhang, Ganesh Ananthanarayanan, Paramvir Bahl, Peter Bodik, Matthai Philipose, Michael J FreedmanAbstract:Video cameras are pervasively deployed for security and smart city scenarios, with millions of them in large cities worldwide. Achieving the potential of these cameras requires efficiently analyzing the live Videos in real-time. We describe VideoStorm, a Video Analytics system that processes thousands of Video Analytics queries on live Video streams over large clusters. Given the high costs of vision processing, resource management is crucial. We consider two key characteristics of Video Analytics: resource-quality tradeoff with multi-dimensional configurations, and variety in quality and lag goals. VideoStorm's offline profiler generates query resource-quality profile, while its online scheduler allocates resources to queries to maximize performance on quality and lag, in contrast to the commonly used fair sharing of resources in clusters. Deployment on an Azure cluster of 101 machines shows improvement by as much as 80% in quality of real-world queries and 7× better lag, processing Video from operational traffic cameras.
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NSDI - Live Video Analytics at scale with approximation and delay-tolerance
2017Co-Authors: Haoyu Zhang, Ganesh Ananthanarayanan, Paramvir Bahl, Peter Bodik, Matthai Philipose, Michael J FreedmanAbstract:Video cameras are pervasively deployed for security and smart city scenarios, with millions of them in large cities worldwide. Achieving the potential of these cameras requires efficiently analyzing the live Videos in real-time. We describe VideoStorm, a Video Analytics system that processes thousands of Video Analytics queries on live Video streams over large clusters. Given the high costs of vision processing, resource management is crucial. We consider two key characteristics of Video Analytics: resource-quality tradeoff with multi-dimensional configurations, and variety in quality and lag goals. VideoStorm's offline profiler generates query resource-quality profile, while its online scheduler allocates resources to queries to maximize performance on quality and lag, in contrast to the commonly used fair sharing of resources in clusters. Deployment on an Azure cluster of 101 machines shows improvement by as much as 80% in quality of real-world queries and 7× better lag, processing Video from operational traffic cameras.
Junchen Jiang - One of the best experts on this subject based on the ideXlab platform.
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HotMobile - Scaling Video Analytics Systems to Large Camera Deployments
Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications, 2019Co-Authors: Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Joseph E. GonzalezAbstract:Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying Video cameras en masse for the spatial monitoring of their physical premises. Scaling Video Analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale Video Analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that Video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera Video Analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling Video Analytics to large camera networks, and outline a plan for its realization.
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ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Xun Zhang, Yuhao Zhou, Joseph E. GonzalezAbstract:Enterprises are increasingly deploying large camera networks for Video Analytics. Many target applications entail a common problem template: searching for and tracking an object or activity of interest (e.g. a speeding vehicle, a break-in) through a large camera network in live Video. Such cross-camera Analytics is compute and data intensive, with cost growing with the number of cameras and time. To address this cost challenge, we present ReXCam, a new system for efficient cross-camera Video Analytics. ReXCam exploits spatial and temporal locality in the dynamics of real camera networks to guide its inference-time search for a query identity. In an offline profiling phase, ReXCam builds a cross-camera correlation model that encodes the locality observed in historical traffic patterns. At inference time, ReXCam applies this model to filter frames that are not spatially and temporally correlated with the query identity's current position. In the cases of occasional missed detections, ReXCam performs a fast-replay search on recently filtered Video frames, enabling gracefully recovery. Together, these techniques allow ReXCam to reduce compute workload by 8.3x on an 8-camera dataset, and by 23x - 38x on a simulated 130-camera dataset. ReXCam has been implemented and deployed on a testbed of 5 AWS DeepLens cameras.
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Scaling Video Analytics Systems to Large Camera Deployments
arXiv: Distributed Parallel and Cluster Computing, 2018Co-Authors: Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Joseph E. GonzalezAbstract:Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying Video cameras en masse for the spatial monitoring of their physical premises. Scaling Video Analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale Video Analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that Video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera Video Analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling Video Analytics to large camera networks, and outline a plan for its realization.
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chameleon scalable adaptation of Video Analytics
ACM Special Interest Group on Data Communication, 2018Co-Authors: Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodik, Siddhartha Sen, Ion StoicaAbstract:Applying deep convolutional neural networks (NN) to Video data at scale poses a substantial systems challenge, as improving inference accuracy often requires a prohibitive cost in computational resources. While it is promising to balance resource and accuracy by selecting a suitable NN configuration (e.g., the resolution and frame rate of the input Video), one must also address the significant dynamics of the NN configuration's impact on Video Analytics accuracy. We present Chameleon, a controller that dynamically picks the best configurations for existing NN-based Video Analytics pipelines. The key challenge in Chameleon is that in theory, adapting configurations frequently can reduce resource consumption with little degradation in accuracy, but searching a large space of configurations periodically incurs an overwhelming resource overhead that negates the gains of adaptation. The insight behind Chameleon is that the underlying characteristics (e.g., the velocity and sizes of objects) that affect the best configuration have enough temporal and spatial correlation to allow the search cost to be amortized over time and across multiple Video feeds. For example, using the Video feeds of five traffic cameras, we demonstrate that compared to a baseline that picks a single optimal configuration offline, Chameleon can achieve 20-50% higher accuracy with the same amount of resources, or achieve the same accuracy with only 30--50% of the resources (a 2-3X speedup).
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SIGCOMM - Chameleon: scalable adaptation of Video Analytics
Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, 2018Co-Authors: Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodik, Siddhartha Sen, Ion StoicaAbstract:Applying deep convolutional neural networks (NN) to Video data at scale poses a substantial systems challenge, as improving inference accuracy often requires a prohibitive cost in computational resources. While it is promising to balance resource and accuracy by selecting a suitable NN configuration (e.g., the resolution and frame rate of the input Video), one must also address the significant dynamics of the NN configuration's impact on Video Analytics accuracy. We present Chameleon, a controller that dynamically picks the best configurations for existing NN-based Video Analytics pipelines. The key challenge in Chameleon is that in theory, adapting configurations frequently can reduce resource consumption with little degradation in accuracy, but searching a large space of configurations periodically incurs an overwhelming resource overhead that negates the gains of adaptation. The insight behind Chameleon is that the underlying characteristics (e.g., the velocity and sizes of objects) that affect the best configuration have enough temporal and spatial correlation to allow the search cost to be amortized over time and across multiple Video feeds. For example, using the Video feeds of five traffic cameras, we demonstrate that compared to a baseline that picks a single optimal configuration offline, Chameleon can achieve 20-50% higher accuracy with the same amount of resources, or achieve the same accuracy with only 30--50% of the resources (a 2-3X speedup).
