Heterogeneous Information Fusion and Visualization for a Large-Scale Intelligent Video Surveillance System

Fan, Ching-Tang; Wang, Yuan-Kai; Huang, Cai-Ren

doi:10.1109/tsmc.2016.2531671

Cited by 67 publications

(27 citation statements)

References 33 publications

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“…With the sensor network, which is a key component of IoT, data can be quickly processed first on local end devices [27]. The connection of IoT devices can support many applications, including Healthcare system [31], [32], [33], Smart city / Smart home [34], [35], [36], Video surveillance [37], [38], [39] and so on. In terms of mobile devices, though some researchers categorize them into edge devices due to its computation power [40], [41], [42], we consider their energy limitation and treat them as end devices in the following discussion.…”

Section: Distributed Computing Hierarchymentioning

confidence: 99%

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

Zhang

Song

Lin

et al. 2022

IEEE Trans. Big Data

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For the pursuit of ubiquitous computing, distributed computing systems containing the cloud, edge devices, and Internet-of-Things devices are highly demanded. However, existing distributed frameworks do not tailor for the fast development of Deep Neural Network (DNN), which is the key technique behind many intelligent applications nowadays. Based on prior exploration on distributed deep neural networks (DDNN), we propose Heterogeneous Distributed Deep Neural Network (HDDNN) over the distributed hierarchy, targeting at ubiquitous intelligent computing. While being able to support basic functionalities of DNNs, our framework is optimized for various types of heterogeneity, including heterogeneous computing nodes, heterogeneous neural networks, and heterogeneous system tasks. Besides, our framework features parallel computing, privacy protection and robustness, with other consideration for the combination of heterogeneous distributed system and DNN. Extensive experiments demonstrate that our framework is capable of utilizing hierarchical distributed system better for DNN and tailoring DNN for real-world distributed system properly, which is with low response time, high performance, and better user experience.Manuscript received xx, xx; revised xx, xx.

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Section: Distributed Computing Hierarchymentioning

confidence: 99%

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

Zhang

Song

Lin

et al. 2022

IEEE Trans. Big Data

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“…Furthermore, the work of Hu and Ni introduced an algorithm for license plate recognition through smart cameras on the basis of a lightweight filter for digital camera sensors to reduce network bandwidth and latency. As far as large metropolitan areas are concerned, data and information fusion techniques on the server/cloud side can be used to properly investigate underlying physical phenomena and events captured by an ISS as discussed in the work of Fan et al Indeed, enhanced elaboration can be performed by exploiting big data techniques as shown in the work of Shao et al, where a novel workflow exploiting (big) surveillance data based on event detection and alarming messages from front‐end smart cameras is proposed.…”

Section: Overview Of the Problem And Related Workmentioning

confidence: 99%

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

et al. 2018

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Summary Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.

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“…Alsmirat [1] utilizes the cloud and mobile edge computing (MEC) to optimize the network bandwidth for wireless surveillance system. Fan [5] presents an event-driven visualization mechanism fusing multi-modal information for a largescale intelligent video surveillance system. Mobile fog [11] proposes a programming model for developing large-scale distributed situation awareness applications, launching the application components on Fog nodes at the edge of the network.…”

Section: Related Workmentioning

confidence: 99%

“…The most intuitive way to implement the idea -At any time point, each camera stores the trajectory of vehicles that are active under its region -is to aggregate the trajectories as vehicle moves from one camera to another. Take the red vehicle in Figure 3 5 as an example:…”

Section: Greedy Trajectory Aggregationmentioning

confidence: 99%

STTR

Gupta

Ramachandran

2018

Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems

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To fully exploit the capabilities of sensors in real life, especially cameras, smart camera surveillance requires the cooperation from both domain experts in computer vision and systems. Existing alert-based smart surveillance is only capable of tracking a limited number of suspicious objects, while in most real-life applications, we often do not know the perpetrator ahead of time for tracking their activities in advance. In this work, we propose a radically different approach to smart surveillance for vehicle tracking. Specifically, we explore a smart camera surveillance system aimed at tracking all vehicles in real time. The insight is not to store the raw videos, but to store the space-time trajectories of the vehicles. Since vehicle tracking is a continuous and geo-distributed task, we assume a geo-distributed Fog computing infrastructure as the execution platform for our system. To bound the storage space for storing the trajectories on each Fog node (serving the computational needs of a camera), we focus on the activities of vehicles in the vicinity of a given camera in a specific geographic region instead of the time dimension, and the fact that every vehicle has a "finite" lifetime. To bound the computational and network communication requirements for detection, re-identification, and inter-node communication, we propose novel techniques, namely, forward and backward propagation that reduces the latency for the operations and the communication overhead. STTR is a system for smart surveillance that we have built embodying these ideas. For evaluation, we develop a toolkit upon SUMO to emulate camera detections from traffic flow and adopt MaxiNet to emulate the fog computing infrastructure on Microsoft Azure.

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Heterogeneous Information Fusion and Visualization for a Large-Scale Intelligent Video Surveillance System

Cited by 67 publications

References 33 publications

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

Towards Ubiquitous Intelligent Computing: Heterogeneous Distributed Deep Neural Networks

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

STTR

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