Anomaly and Activity Recognition Using Machine Learning Approach for Video Based Surveillance

Mohan, Aiswarya; Choksi, Meghavi; Zaveri, Mukesh A.

doi:10.1109/icccnt45670.2019.8944396

Cited by 19 publications

(15 citation statements)

References 18 publications

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“…In the next year 2019, the first paper proposed a system including CNN as their main framework for detecting anomalies and SI and MHOF they had used as a spark ignition model along with tackling a few challenges associated with RGB levels and their edges and tracking of objects from angle derivations from multiple camera feeds. They impressed principal component analysis for obtaining a higher-graded feature [5]. In another paper of 2019, the proposed system was trained and tested on three different datasets as only one dataset was not efficient for checking the efficiency of the model.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Anomaly Detection in Video Surveillance: A Mathematical Modeling and Nonlinear Analysis Perspective with MobileNet and Bi-LSTM

Deepak Mane

2024

cana

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For the main purpose of security and safety, video surveillance systems are everywhere and their need for presence is at the top. The prominent application of a video monitoring system is to track and monitor the live footage and recognize any unusual activity or behavior. For the prevention of any unwanted event or dangerous activity, it is mandatory to get early detection from video surveillance. The previous architectures of traditional systems were primarily based on manual monitoring anomaly detection which had increased the false negative rate of anomaly detection. In this paper, we proposed a customized framework that leverages MobileNet and Bi-LSTM for the accurate detection of anomalies in real-time with unsupervised learning techniques. Due to the spatial-temporal feature pattern and multi-modal functionality of MobileNet, it helps in increasing accuracy compared to previous systems. MobileNet uses depth-wise and point-wise convolution than the normal convolution which improves the performance metrics. This paper has illustrated a brief study of benchmark datasets such as UCF dataset including their performance in anomaly detection compared with traditional systems and existing deep learning approaches. The proposed model has given an accuracy of 95.33% compared to the latest year 2023 model which gave an accuracy of 90.80% on the same dataset.

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Section: Related Workmentioning

confidence: 99%

Real-Time Anomaly Detection in Video Surveillance: A Mathematical Modeling and Nonlinear Analysis Perspective with MobileNet and Bi-LSTM

Deepak Mane

2024

cana

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“…3 Moreover, the criminal activities are identified by analyzing a specific person. 4 The fundamentals of crime detection are detailed in Figure 1. The collected videos are applied to the system, and then the videos are changed into image format.…”

Section: Introductionmentioning

confidence: 99%

Crime activities prediction system in video surveillance by an optimized deep learning framework

Kumar¹,

Reddy²

2022

Concurrency and Computation

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The video surveillance technology is used to find crime events in public places and capture live public events. Hence, detecting the criminalist before the crime actions is the most needed event to catch the criminalist. However, the presence of noise content in the trained video has raised the difficulties in crime specification by maximizing the complexity range of the data. To overcome this issue, this research has designed a novel lion-based deep belief neural paradigm (LbDBNP) to identify criminals by their activities and handling tools. Initially, three types of datasets were trained to the system then the training flaws were eliminated in the preprocessing layer. Hereafter, the cleaned data is imported to the classification module to detect the crime events present in the video. Subsequently, the designed model is implemented using the Python framework in Windows 10 platform. To evaluate the efficiency of the designed model, the attack is launched in the proposed model after those metrics are calculated. In addition, the robustness of the designed system is verified by three datasets, such as UCSDped1, UCSDped2, and avenue crime. Also, the key parameters of the designed model have been evaluated and compared with other existing schemes to verify the proposed model's robustness by achieving the finest outcomes.

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“…Local and global anomaly detection method using hierarchical feature representation and Gaussian process regression was introduced in [19], where global anomalies refer to the anomalies among sequence frames and local anomalies denote the anomalous regions within a frame. Local anomalies are commonly detected in videos based on local spatio-temporal features, where motion is occurring and generating due to multiple objects moving within single scene [20]. Video anomaly detection methods are interested in determining whether the current frame of a given video demonstrates an anomaly or not [20], [21].…”

Section: Introductionmentioning

confidence: 99%

“…Local anomalies are commonly detected in videos based on local spatio-temporal features, where motion is occurring and generating due to multiple objects moving within single scene [20]. Video anomaly detection methods are interested in determining whether the current frame of a given video demonstrates an anomaly or not [20], [21].…”

Section: Introductionmentioning

confidence: 99%

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Anomaly event detection and localization of video clips using global and local outliers

Mahmood

Abid

Lafta

2021

IJEECS

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The automatic detection of anomaly events in video sequence has become a critical issue and essential demand for the extensive deployment of computer vision systems such as video surveillance applications. An anomaly event in video can be denoted as outlier behavior within video frames which formulated by a deviation from the stable scene. In this paper, an anomaly event detection and localization method in video sequence is presented including multilevel strategy as temporal frames differences estimation, modelling of normal and abnormal behavior using regression model and finally density–based clustering to detect the outliers (abnormal event) at clips level. Hence, outlier score is obtained at the segment or clip level along video frames sequences. The proposed method seplits video frames into nonoverlapped clips using global outlier detection process. Afterward, at each clip, the local outliers are determined based on density of each clip. Extensive experiments were conducted upon two public video datasets which include dense and scattered outliers along video sequence. The experiments were performed on two common public datasets (Avenue) and University of California, San Diego (UCSD). The experimental results exhibited that the proposed method detects well outlier frames at clip level with lower computational complexity comparing to the state-of-the-art methods.

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Anomaly and Activity Recognition Using Machine Learning Approach for Video Based Surveillance

Cited by 19 publications

References 18 publications

Real-Time Anomaly Detection in Video Surveillance: A Mathematical Modeling and Nonlinear Analysis Perspective with MobileNet and Bi-LSTM

Real-Time Anomaly Detection in Video Surveillance: A Mathematical Modeling and Nonlinear Analysis Perspective with MobileNet and Bi-LSTM

Crime activities prediction system in video surveillance by an optimized deep learning framework

Anomaly event detection and localization of video clips using global and local outliers

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