Designing and Building Context-Aware Services: The ContextServ Project

Sheng, Quan Z.; Yu, Jian; Zhang, Wei Emma; Wang, Shuang; Li, Xiaoping; Benatallah, Boualem

doi:10.1007/978-3-030-73203-5_11

Cited by 2 publications

(1 citation statement)

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“…In order to overcome this issue, a growing number of research papers have focused on automatically recognising equipment activity from site surveillance recordings [8]. Devices used for video surveillance are typically placed in harsh environments and sparsely inhabited areas, making it impossible for them to process video events and offer consistent and dependable data communication [9]. Academics and business have put up a number of efficient and sustainable solutions to address these issues, including energy‐efficient IoT, remote microgrids, fog data centres, self‐sustaining wireless sensor nodes, dense mobile crowd sensing, sustainable data links, etc.…”

Section: Introductionmentioning

confidence: 99%

Abnormal event detection model using an improved ResNet101 in context aware surveillance system

Kalshetty¹,

Parveen²

2023

Cognitive Comp and Systems

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Surveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance‐system utilising hybrid ResNet101‐ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre‐processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre‐processed frame is fetched into hybrid ResNet101‐ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal‐events detected the context aware services generate alert to the user for preventing abnormal‐activities. Accuracy, precision, recall, and error values reached for the proposed‐model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.

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Section: Introductionmentioning

confidence: 99%