Harvest the Information from Multimedia Big Data in Global Camera Networks

Su, Wei-Tsung; Lu, Yung-Hsiang; Kaseb, Ahmed S.

doi:10.1109/bigmm.2015.55

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…A recent statistical study has revealed more than 300 hours of video content are added to YouTube every minute [1]. Moreover, a recent survey on network cameras has indicated that a staggering 28 million network cameras will be sold in 2017 alone [59]. Given the steep growth in video content all over the world, the capability of modern computers to process video data and extract information from them remains a huge challenge.…”

Section: Introductionmentioning

confidence: 99%

Rank Pooling for Action Recognition

Fernando

Gavves

Oramas

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

290

218

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We propose a function-based temporal pooling method that captures the latent structure of the video sequence data -e.g. how frame-level features evolve over time in a video. We show how the parameters of a function that has been fit to the video data can serve as a robust new video representation. As a specific example, we learn a pooling function via ranking machines. By learning to rank the frame-level features of a video in chronological order, we obtain a new representation that captures the video-wide temporal dynamics of a video, suitable for action recognition. Other than ranking functions, we explore different parametric models that could also explain the temporal changes in videos. The proposed functional pooling methods, and rank pooling in particular, is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions. We evaluate our method on various benchmarks for generic action, fine-grained action and gesture recognition. Results show that rank pooling brings an absolute improvement of 7-10 average pooling baseline. At the same time, rank pooling is compatible with and complementary to several appearance and local motion based methods and features, such as improved trajectories and deep learning features. *

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Section: Introductionmentioning

confidence: 99%

Rank Pooling for Action Recognition

Fernando

Gavves

Oramas

et al. 2017

IEEE Trans. Pattern Anal. Mach. Intell.

290

218

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“…This paper extends our previous work [10,18,31] that builds a software infrastructure using cloud computing to analyze visual data from thousands of network cameras. The system is referred to as Continuous Analysis of Many CAMeras (CAM 2 ).…”

Section: Improvements From Our Previous Workmentioning

confidence: 54%

“…T HE use of visual data such as images and videos for scientific analysis to solve real-world problems has been increasing significantly over the past decade. Network cameras are of particular interest as they generate continuous real-time video data with rich and versatile content [31]. Millions of network cameras are deployed every year [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Mohan

Kaseb

et al. 2021

IEEE Trans. Cloud Comput.

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There has been tremendous growth in the amount of visual data available on the Internet in recent years. One type of visual data of particular interest is produced by network cameras providing real-time views. Millions of network cameras around the world continuously stream data to viewers connected to the Internet. This data may be used by a wide variety of applications such as enhancing public safety, urban planning, emergency response, and traffic management which are computationally intensive. Analyzing this data requires significant amounts of computational resources. Cloud computing can be a preferred solution for meeting the resource requirements for analyzing these data. There are many options when selecting cloud instances (amounts of memory, number of cores, locations, etc.). Inefficient provisioning of cloud resources may become costly in pay-per-use cloud computing. This paper presents a method to select cloud instances in order to meet the performance requirements for visual data analysis at a lower cost. We measure the frame rates when analyzing the data using different computer vision methods and model the relationships between frame rates and resource utilizations. We formulate the problem of managing cloud resources as a Variable Size Bin Packing Problem and use a heuristic solution. Experiments using Amazon EC2 validate the model and demonstrate that the proposed solution can reduce the cost up to 62% while meeting the performance requirements.

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“…Recently, Alam and colleagues [3] demonstrated a prototype showing drivers road conditions from real-time images captured by traffic cameras. Su and colleagues [8] suggested creating a system that can harvest data from a wide range of network cameras, not limited to traffic cameras. However, the fusion of physical media (e.g., video feeds) and social media has yet to be explored, especially in the emergency and natural disaster domain.…”

Section: A Images and Videos For Emergency Response And Public Safetymentioning

confidence: 99%

Cross-referencing social media and public surveillance camera data for disaster response

Surakitbanharn

Yau

Wang

et al. 2018

2018 IEEE International Symposium on Technologies for Homeland Security (HST)

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Physical media (like surveillance cameras) and social media (like Instagram and Twitter) may both be useful in attaining on-the-ground information during an emergency or disaster situation. However, the intersection and reliability of both surveillance cameras and social media during a natural disaster are not fully understood. To address this gap, we tested whether social media is of utility when physical surveillance cameras went off-line during Hurricane Irma in 2017. Specifically, we collected and compared geo-tagged Instagram and Twitter posts in the state of Florida during times and in areas where public surveillance cameras went off-line. We report social media content and frequency and content to determine the utility for emergency managers or first responders during a natural disaster.

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Harvest the Information from Multimedia Big Data in Global Camera Networks

Cited by 8 publications

References 19 publications

Rank Pooling for Action Recognition

Rank Pooling for Action Recognition

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Cross-referencing social media and public surveillance camera data for disaster response

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