Motion detection in video surviellance: A systematic survey

Savitha, C K; Ramesh, D.

doi:10.1109/icisc.2018.8398880

Cited by 12 publications

(6 citation statements)

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“…Consequently, in this paper, we provide a comprehensive survey of abnormality detection and the algorithms that can aid in detecting the abnormality. In the past decade, numerous books, review articles, and surveys ranging from brief to comprehensive have been published in anomaly detection, 12,[35][36][37][38][39][40][41][42][43][44][45] but our survey is relevant and different from others in many aspects. For instance, Nandhini Abirami et al 46 only targeted the deep CNN and deep GAN in their survey.…”

Section: Rule-based Classificationmentioning

confidence: 99%

“…Due to the extensive research in anomaly detection, the author in Ref. 45 provided a brief systematic survey of motion and anomaly detection in video surveillance and discussed related works in a similar area. In addition, a simple and well-explained methodology for behavior analysis was also discussed.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in Ref. 45 provided a brief systematic survey of motion and anomaly detection in video surveillance. However, our survey is detailed and systematically ordered.…”

Section: Introductionmentioning

confidence: 99%

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Anomaly behavior detection analysis in video surveillance: a critical review

et al. 2023

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Anomaly detection is one of the most researched topics in computer vision and machine learning. Manual detection of an oddity in a video costs significant time and money, so there is a need for an autonomous detection system that can analyze the process and detect the anomaly in the majority of captured video datasets. Through an in-depth study on the recently published works on anomaly detection, a review is prepared to highlight the various tasks performed in abnormal behavior detection. Descriptions along with the pros and cons of various machine-learning and non-machine-learning techniques are discussed in depth. Similarly, more concentration is given to the generation adversarial network (GAN), and a comprehensive description of its design for achieving a better abnormality detection rate is provided. Moreover, a comparison of various state-of-the-art approaches on the basis of their methodologies, advantages, and disadvantages is given. We further quantitatively analyze some of the recent robust approaches at the frame level on the UCSD Ped1 dataset, with the GAN-based model achieving an astonishing performance. We provide various suggestions on how to further increase the performance of GAN for abnormal behavior detection in surveillance videos.

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Section: Rule-based Classificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Anomaly behavior detection analysis in video surveillance: a critical review

et al. 2023

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“…Various visual tracking architectures have been proposed such as those based on convolutional neural networks and particle filtering and colored area tracking using mean shift tracking through temporal image sequence [12]. A survey of approaches for intruder detection systems in a camera monitored frame for surveillance was explained by C. Savitha and D. Ramesh [13]. A. Shahbaz and K. Jo also proposed a human verifier which is a SVM classifier based on histogram of oriented gradients along with an algorithm for change detection based on Gaussian mixture model [14].…”

Section: Related Workmentioning

confidence: 99%

Articulated Human Pose Estimation Using Greedy Approach

Kherwa¹,

Singh²,

Saheel³

et al. 2021

Artificial Intelligence

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The goal of this Chapter is to introduce an efficient and standard approach for human pose estimation. This approach is based on a bottom up parsing technique which uses a non-parametric representation known as Greedy Part Association Vector (GPAVs), generates features for localizing anatomical key points for individuals. Taking leaf out of existing state of the art algorithm, this proposed algorithm aims to estimate human pose in real time and optimize its results. This approach simultaneously detects the key points on human body and associates them by learning the global context. However, In order to operate this in real environment where noise is prevalent, systematic sensors error and temporarily crowded public could pose a challenge, an efficient and robust recognition would be crucial. The proposed architecture involves a greedy bottom up parsing that maintains high accuracy while achieving real time performance irrespective of the number of people in the image.

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“…In Industry 4.0, following the growing public demand for industrial products that significantly contribute to economic perspectives [1,2], manufacturers are striving to produce highly customizable products while maintaining production accuracy and speed [2,3]. In line with this, government regulations have been established with the purpose of regulating the responsibility of company owners to ensure worker safety during working hours [4]. Likewise, monitoring manufacturing environments, particularly human workers, is crucial [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Detection of Anomalous Behavior of Manufacturing Workers Using Deep Learning-Based Recognition of Human–Object Interaction

2023

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The increasing demand for industrial products has expanded production quantities, leading to negative effects on product quality, worker productivity, and safety during working hours. Therefore, monitoring the conditions in manufacturing environments, particularly human workers, is crucial. Accordingly, this study presents a model that detects workers’ anomalous behavior in manufacturing environments. The objective is to determine worker movements, postures, and interactions with surrounding objects based on human–object interactions using a Mask R-CNN, MediaPipe Holistic, a long short-term memory (LSTM), and worker behavior description algorithm. The process begins by recognizing the objects within video frames using a Mask R-CNN. Afterward, worker poses are recognized and classified based on object positions using a deep learning-based approach. Next, we identified the patterns or characteristics that signified normal or anomalous behavior. In this case, anomalous behavior consists of anomalies correlated with human pose recognition (emergencies: worker falls, slips, or becomes ill) and human pose recognition with object positions (tool breakage and machine failure). The findings suggest that the model successfully distinguished anomalous behavior and attained the highest pose recognition accuracy (approximately 96%) for standing, touching, and holding, and the lowest accuracy (approximately 88%) for sitting. In addition, the model achieved an object detection accuracy of approximately 97%.

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Motion detection in video surviellance: A systematic survey

Cited by 12 publications

References 12 publications

Anomaly behavior detection analysis in video surveillance: a critical review

Anomaly behavior detection analysis in video surveillance: a critical review

Articulated Human Pose Estimation Using Greedy Approach

Detection of Anomalous Behavior of Manufacturing Workers Using Deep Learning-Based Recognition of Human–Object Interaction

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