Abnormal Human Behavior Detection in Videos: A Review

Mu, Huiyu; Sun, Ruizhi; Yuan, Gang; Wang, Yun

doi:10.5755/j01.itc.50.3.27864

Cited by 13 publications

(10 citation statements)

References 145 publications

(209 reference statements)

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“…The following 1 denotes a single classification, and the last 1 is confidence. After that, modify some network parameters, set stride and padding to 1, filter to 10, mask to (6,7,8), The anchor is set to (10,13,16,30)…”

Section: Implementation Detailsmentioning

confidence: 99%

“…With the development of convolutional neural networks (CNNs) [14], the above-mentioned problems have been solved. CNN-based methods use stacked convolution and pooling to extract distinguishable and robust features from a variety of data and have made unprecedented achievements in the field of image recognition [13]. In addition, CNN-based methods have also achieved excellent performance in object detection and even surpassed human beings in some aspects [6].…”

Section: Introductionmentioning

confidence: 99%

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Human Detection Algorithm Based on Improved YOLO v4

Zhou¹,

Xie³

et al. 2022

ITC

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The human behavior datasets have the characteristics of complex background, diverse poses, partial occlusion, and diverse sizes. Firstly, this paper adopts YOLO v3 and YOLO v4 algorithms to detect human objects in videos, and qualitatively analyzes and compares detection performance of two algorithms on UTI, UCF101, HMDB51 and CASIA datasets. Then, this paper proposed an improved YOLO v4 algorithm since the vanilla YOLO v4 has incomplete human detection in specific video frames. Specifically, the improved YOLO v4 introduces the Ghost module in the CBM module to further reduce the number of parameters. Lateral connection is added in the CSP module to improve the feature representation capability of the network. Furthermore, we also substitute MaxPool with SoftPool in the primary SPP module, which not only avoids the feature loss, but also provides a regularization effect for the network, thus improving the generalization ability of the network. Finally, this paper qualitatively compares the detection effects of the improved YOLO v4 and primary YOLO v4 algorithm on specific datasets. The experimental results show that the improved YOLO v4 can solve the problem of complex targets in human detection tasks effectively, and further improve the detection speed.

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Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human Detection Algorithm Based on Improved YOLO v4

Zhou¹,

Xie³

et al. 2022

ITC

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show abstract

“…Mediface Sample -Transferred [39] Show a unified (temporal and spatial) model of form and movement (Mixture of dynamic texture) It's challenging to make direct comparisons between motion and appearance representations, which are usually adapted to a certain scene domain. [40] The foreground was extracted using a double filtering technique, and the noise was diminished using a media filter.…”

Section: Deep Metric Learning To Take Use Of Advantage Of Label Corre...mentioning

confidence: 99%

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

Ya-meng¹,

Wang²,

Shen-shen³

2022

IJACSA

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The analysis of human movement has attracted the attention of many scholars of various disciplines today. The purpose of such systems is to perceive human behavior from a sequence of video images. They monitor the population to find common properties among pedestrians on the scene. In video surveillance, the main purpose of detecting specific or malicious events is to help security personnel. Different methods have been used to detect human behavior from images. This paper has used an efficient computational algorithm for detecting anomalies in video images based on the combined approach of the differential evolution algorithm and cellular neural network. In this method, the input image's gray-level image is first generated. Because it may be possible to identify several large areas in the image after the threshold, the largest white area is selected as the target area. The images are then used to remove noise, smooth the image, and fade the morphology. The results showed that the proposed method has higher speed and accuracy than other methods. The advantage of the algorithm is that it has a runtime of three seconds on a home computer, and the average sensitivity criterion is 98.6% (97.2%).

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“…These papers are more suitable for advanced study and not for new researches. The survey by [10] stressed on video-based ABD and only a few ADLs such as were mentioned. [11], [12] emphasized only deep learning methods that are popular in the field of ABD.…”

Section: Introductionmentioning

confidence: 99%

A Review of Abnormal Behavior Detection in Activities of Daily Living

et al. 2023

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Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend. INDEX TERMS abnormal behavior detection, activities of daily living

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Abnormal Human Behavior Detection in Videos: A Review

Cited by 13 publications

References 145 publications

Human Detection Algorithm Based on Improved YOLO v4

Human Detection Algorithm Based on Improved YOLO v4

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

A Review of Abnormal Behavior Detection in Activities of Daily Living

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