Hybrid approach for suspicious object surveillance using video clips and UAV images in cloud-IoT-based computing environment

Ahamad, Rayees; Mishra, Kamta Nath

doi:10.1007/s10586-023-03977-0

Cited by 13 publications

(2 citation statements)

References 76 publications

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“…Ahamad [1] suggested a hybrid video surveillance system for object detection. The different frames of video were processed using the Open-source Control Vision software.…”

Section: Literature Reviewmentioning

confidence: 99%

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Jayaram C V

2024

IJRITCC

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Co-located pattern recognition is the process of identifying the sequence of patterns occurring in surveillance videos. In greater part of the existing works, the detection of rare and popular events for effective co-located pattern recognition is not concentrated. Therefore, this paper presents the automatic discovery of the co-located patterns based on rare and popular events in the video. First, the video is converted to frames, and the keyframes are preprocessed. Then, the foreground and background of the frames are estimated, and the rare and popular events are grouped using Maximum-Minimum Pixel-Per-Inch Density-Based Spatial Clustering of Applications with Noise (Max-MinPPI-DBSCAN). From the grouped image, the object detection and mapping are done, and the patch is extracted from it. Next, the edges are detected and from that, for the moving objects, motion is estimated by the Kullback-Leibler Kalman Filter (KLKF). Also, for non-moving objects, the objects/persons are tracked. From the motion estimated and tracked data, time series features are extracted. Then, the optimal features are selected using the Dung Beetle State Transition Probability Optimizer (DBSTPO). Finally, the co-located pattern is classified using a Generalized Recurrent Extreme Value Neural Network (GREVNN), and the alert message is given to the authorities. Hence, the proposed model selected the features in 53239.44ms and classified the event with 99.0723% accuracy and showed better performance than existing works.

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“…Ahamad [1] suggested a hybrid video surveillance system for object detection. The different frames of video were processed using the Open-source Control Vision software.…”

Section: Literature Reviewmentioning

confidence: 99%

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Jayaram C V

2024

IJRITCC

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“…In modern day era, publishing research trends for "smart city", "IoT-based video surveillance", "IoT-based Smart City Surveillance" as well as "IoT in smart cities" have evolved exponentially [6]. Numerous researchers have recommended several ways to improve the smartness of a city [7].…”

Section: Introductionmentioning

confidence: 99%

Smart surveillance using IoT: a review

Sharma,

Kanwal

2024

reks

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In today’s modern society, video surveillance is a growing trend and it can revolutionize many aspects of technology, especially in future smart cities that will transform traditional surveillance systems into intelligent and interconnected networks. It may be difficult for even well-trained employees to process and respond immediately to monitored data. Moreover, IoT-enabled surveillance systems overcome the challenges and flaws of conventional passive monitoring techniques by offering real-time surveillance and automated notifications for suspicious activity, intrusions, or anomalies. Therefore, the objective of this article is to provide an Internet of Things (IoT)-based smart surveillance system that may successfully connect the ecosystem, resulting in enhanced monitoring for smart city services. The primary goal of this study is to provide a comprehensive review of several IoT-based surveillance techniques used in various smart city applications. The categorization of tasks for each section is as follows: to present the historical context overview; to examine the significance of IoT and its application in smart cities; to present a standardized architecture for IoT-based smart city surveillance; to highlight an authoritative and thorough review of current IoT-based smart surveillance systems; and to identify various research issues. The methods used are: statistical graph or chart approach, schematic, and timeline diagram. Conclusions. This article outlines numerous research challenges for future video surveillance that may be addressed by researchers. In summary, this comprehensive review provides a valuable and streamlined resource for future researchers exploring smart city surveillance through the IoT.

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