A Survey of Video-Based Crowd Anomaly Detection in Dense Scenes

Ma, Junjie; Hirota, Kaoru

doi:10.20965/jaciii.2017.p0235

Cited by 32 publications

(13 citation statements)

References 54 publications

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“…With the constant increase in the number of cameras deployed for surveillance purposes, the surveillance community has noticed the demand for human resources to process video stream data to make decisions timely [6,7]. The conventional solutions rely on a cloud computing platform for the pervasive deployment of networked cameras, either static or mobile, which create a huge amount of surveillance data and atomize the video processing [8,9]. Object detection using machine learning (ML) [10] and statistical analysis [11] approaches are of main interest in recent years.…”

Section: Smart Surveillance Systemsmentioning

confidence: 99%

BlendSPS: A BLockchain-ENabled Decentralized Smart Public Safety System

Nikouei

Nagothu

et al. 2020

Smart Cities

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Due to the recent advancements in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, the Smart Public Safety (SPS) system has become a more realistic solution for seamless public safety services that are enabled by integrating machine learning (ML) into heterogeneous edge computing networks. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service-oriented architecture (SOA) is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on a centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging the hybrid blockchain fabric, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability, and traceability for secure data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed BlendSPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

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Section: Smart Surveillance Systemsmentioning

confidence: 99%

BlendSPS: A BLockchain-ENabled Decentralized Smart Public Safety System

Nikouei

Nagothu

et al. 2020

Smart Cities

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“…The observation network knows about the developing demand for HR to translate information, for example, live video streams [8]. The universal organization of arranged staticand versatile cameras makes a tremendous measure of video datathat is being transmitted to server farms for investigation [9] and atomize the procedure [10]. Many robotized object detection algorithms have been examined utilizing ML [11] and statistical analysis [12] approaches that are actualized at the server side of the observation system.…”

Section: Literature Surveymentioning

confidence: 99%

Improved Accuracy of Suspicious Activity Detection in Surveillance Video

Gurav,

Godbole,

Sonale

2020

IJEAT

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Suspicious activity detection from surveillance video is the main objective of the work presented in this paper. The method developed consist of various stages of suspicious frame detection, and verifying the frame for suspicious activity related analysis of human movements within obtained set of suspicious frames. The method consist of GLCM feature extraction which constitutes the features such as energy, prominence, contrast, entropy, homogeneity type of features and matching using Euclidian distance along with descriptor features obtained by using Harris corner features and cosine similarity index estimation. The successful suspicious activity detection rate is analyzed which shows better performance and time saving method while analyzing large surveillance video dataset.

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“…To detect anomalies, machine learning or deep learning models can be trained using only the data from normal observations (that are generally abundantly available) and these algorithms can then flag any significant deviations as anomalous behaviour [14]. Computer vision techniques have been successfully used in identifying anomalous behaviours in homes [15], crowded scenes [16] and public areas [17]. There has also been a lot of work in the general field of video based anomaly detection using deep learning methods [18], [19].…”

Section: Introductionmentioning

confidence: 99%