VEKG: Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern Matching

Yadav, Piyush; Curry, Edward

doi:10.1109/gc46384.2019.00011

Cited by 18 publications

(17 citation statements)

References 28 publications

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“…One of the most interesting applications of CEP engines is their ability to detect complex events in video streams. Although there are several engines that can handle the uncertainties involved in complex events, there is still a gap in handling uncertainties in video CEP engines, such as VidCEP [20], Eventnet [81], and video event knowledge graph (VEKG) CEP [82]. Complex event detection in video streams involves various objects with different confidence levels, spatial relationships, and temporal relationships.…”

Section: Discussionmentioning

confidence: 99%

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Honarparvar

Saeedi

Liang

et al. 2021

IJGI

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Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute simple events, missing objects result in missing simple events, thus the number of detected complex events. As the main objective of this paper, an integrated cloud and edge computing architecture was designed and developed to reduce missing simple events. To achieve this goal, we deployed multiple smart cameras (i.e., cameras which connect to the Internet and are integrated with computerised systems such as the DL unit) in order to detect complex events from multiple views. Having more simple events from multiple cameras can reduce missing simple events and increase the number of detected complex events. To evaluate the accuracy of complex event detection, the F-score of risk behaviour regarding COVID-19 spread events in video streams was used. The experimental results demonstrate that this architecture delivered 1.73 times higher accuracy in event detection than that delivered by an edge-based architecture that uses one camera. The average event detection latency for the integrated cloud and edge architecture was 1.85 times higher than that of only one camera. However, this finding was insignificant with regard to the current case study. Moreover, the accuracy of the architecture for complex event matching with more spatial and temporal relationships showed significant improvement in comparison to the edge computing scenario. Finally, complex event detection accuracy considerably depended on object detection accuracy. Regression-based models, such as you only look once (YOLO), were able to provide better accuracy than region-based models.

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Section: Discussionmentioning

confidence: 99%

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Honarparvar

Saeedi

Liang

et al. 2021

IJGI

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“…The video event is a high-level semantic concept captured due to the change of state in video content while observed over time [32]. Using CEP analogy, two categories of video events are defined [38].…”

Section: A Video Event Definitionmentioning

confidence: 99%

“…These frames need to be converted into a structured representation which can act as input to the CEP engine. From the representation perspective, a video can be divided into two aspects: 1) Objects, and 2) Relationships among Objects [38].…”

Section: B Video Event Representationmentioning

confidence: 99%

“…4. VEKG graph for each video frame [38] Fig . 4 shows a VEKG stream of three VEKG graphs for image frames at different time instances.…”

Section: B Video Event Representationmentioning

confidence: 99%

“…Average event representation time for different video frames[38] Average query accuracy and matcher confidence score Event latency (system) for different time windows with error bars…”

mentioning

confidence: 99%

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VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Yadav

Curry

2019

2019 IEEE International Conference on Big Data (Big Data)

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Video data is highly expressive and has traditionally been very difficult for a machine to interpret. Querying event patterns from video streams is challenging due to its unstructured representation. Middleware systems such as Complex Event Processing (CEP) mine patterns from data streams and send notifications to users in a timely fashion. Current CEP systems have inherent limitations to query video streams due to their unstructured data model and lack of expressive query language. In this work, we focus on a CEP framework where users can define high-level expressive queries over videos to detect a range of spatiotemporal event patterns. In this context, we propose-i) VidCEP, an in-memory, on the fly, near real-time complex event matching framework for video streams. The system uses a graphbased event representation for video streams which enables the detection of high-level semantic concepts from video using cascades of Deep Neural Network models, ii) a Video Event Query language (VEQL) to express high-level user queries for video streams in CEP, iii) a complex event matcher to detect spatiotemporal video event patterns by matching expressive user queries over video data. The proposed approach detects spatiotemporal video event patterns with an F-score ranging from 0.66 to 0.89. VidCEP maintains near real-time performance with an average throughput of 70 frames per second for 5 parallel videos with sub-second matching latency.

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Grid sampling based hypergraph matching technique for multiple objects tracking in video frames

Srinivasan,

Doraipandiyan,

Lakshmi

et al. 2023

Multimed Tools Appl

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VEKG: Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern Matching

Cited by 18 publications

References 28 publications

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

Design and Development of an Internet of Smart Cameras Solution for Complex Event Detection in COVID-19 Risk Behaviour Recognition

VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

Grid sampling based hypergraph matching technique for multiple objects tracking in video frames

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