A system for large-scale analysis of distributed cameras

Kaseb, Ahmed S.; Berry, Everett; Koh, Youngsol; Mohan, Ashwin; Chen, Wenyi; He, Li; Lu, Yung-Hsiang; Delp, Edward J.

doi:10.1109/globalsip.2014.7032135

Cited by 25 publications

(7 citation statements)

References 10 publications

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“…One application of real-time data is the detection of areas affected by natural disasters. This section describes a project called the Continuous Analysis of Many Cameras (CAM2) [18], which acquires and processes real-time data from network cameras. …”

Section: G Cocomentioning

confidence: 99%

Comparison of Visual Datasets for Machine Learning

Gauen

Dailey

Laiman

et al. 2017

2017 IEEE International Conference on Information Reuse and Integration (IRI)

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Abstract-One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new approach creating datasets using real-time, geo-tagged visual data, greatly improving the contextual information of the data. The data could be automatically labeled by cross-referencing information from other sources (such as weather).

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Section: G Cocomentioning

confidence: 99%

Comparison of Visual Datasets for Machine Learning

Gauen

Dailey

Laiman

et al. 2017

2017 IEEE International Conference on Information Reuse and Integration (IRI)

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“…Online surveillance systems gather image streams from multiple cameras, and operate on images from different streams in parallel [10]. Processing each image represents a narrow task.…”

Section: Dctmentioning

confidence: 99%

Pagoda

Yeh

Sabne

Sakdhnagool

et al. 2017

Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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Massively multithreaded GPUs achieve high throughput by running thousands of threads in parallel. To fully utilize the hardware, workloads spawn work to the GPU in bulk by launching large tasks, where each task is a kernel that contains thousands of threads that occupy the entire GPU.GPUs face severe underutilization and their performance benefits vanish if the tasks are narrow, i.e., they contain < 500 threads. Latency-sensitive applications in network, signal, and image processing that generate a large number of tasks with relatively small inputs are examples of such limited parallelism. This paper presents Pagoda, a runtime system that virtualizes GPU resources, using an OS-like daemon kernel called MasterKernel. Tasks are spawned from the CPU onto Pagoda as they become available, and are scheduled by the MasterKernel at the warp granularity. Experimental results demonstrate that Pagoda achieves a geometric mean speedup of 5.70x over PThreads running on a 20-core CPU, 1.51x over CUDA-HyperQ, and 1.69x over GeMTC, the state-of-the-art runtime GPU task scheduling system.

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“…The computing platform contains three major components, all of which enables users to run analysis programs on cloud servers. [12]. (1) Analysis programs are either created by users or selected from an available list of provided programs.…”

Section: System Architecturementioning

confidence: 99%

See the World Through Network Cameras

Thiruvathukal

Kaseb

et al. 2019

Computer

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Millions of network cameras have been deployed worldwide. Real-time data from many network cameras can offer instant views of multiple locations with applications in public safety, transportation management, urban planning, agriculture, forestry, social sciences, atmospheric information, and more. This paper describes the real-time data available from worldwide network cameras and potential applications. Second, this paper outlines the CAM 2 System available to users at https: //www.cam2project.net/. This information includes strategies to discover network cameras and create the camera database, user interface, and computing platforms. Third, this paper describes many opportunities provided by data from network cameras and challenges to be addressed.

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A system for large-scale analysis of distributed cameras

Cited by 25 publications

References 10 publications

Comparison of Visual Datasets for Machine Learning

Comparison of Visual Datasets for Machine Learning

Pagoda

See the World Through Network Cameras

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