Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Zhang, Tianhao; Aftab, Waqas; Mihaylova, Lyudmila; Langran-Wheeler, Christian; Rigby, S.E.; Fletcher, D. I.; Maddock, Steve; Bosworth, Garry

doi:10.3390/s22124324

Cited by 24 publications

(9 citation statements)

References 150 publications

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“…Other representative RCNN based models include the one proposed by Ballas et al [ 273 ], MaskRNN [ 274 ], and MoNet [ 275 ]. For comprehensive discussions of video analytics we refer to recent surveys [ 270 , 271 , 276 , 277 ].…”

Section: Deep Learning In Diverse Intelligent Sensor Based Systemsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.

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Section: Deep Learning In Diverse Intelligent Sensor Based Systemsmentioning

confidence: 99%

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu

Wang

Yin

et al. 2022

Sensors

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“…Yet another video-analytics-based work was discussed for the security and surveillance of rail networks to monitor trespasser and suicide actions [18]. As the survey work, it listed challenges faced to monitor rail networks with video sensors, the merits of such ideas and demerits caused, etc.…”

Section: Related Workmentioning

confidence: 99%

Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video

Selvi¹,

Adimoolam

Govindharaju

et al. 2022

Electronics

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Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations.

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“…Similar findings are reported in a recent Swedish study, where mid-track fencing at a station reduced suicides there by 62.5%, although there were some concerns about substitution effects, whereby an increase was observed in nearby stations that functioned as a control group [ 18 ]. Promising new techniques, including advanced sensor surveillance [ 19 ], video monitoring [ 20 ] and the effect of lighting [ 21 , 22 ] have been subjected to theoretical examination and assessed in feasibility studies, but evidence for their effects on a larger scale remains limited.…”

Section: Introductionmentioning

confidence: 99%

Sociodemographic and psychosocial risk factors of railway suicide: a mixed-methods study combining data of all suicide decedents in the Netherlands with data from a psychosocial autopsy study

Balt,

Mérelle,

Popma

et al. 2024

BMC Public Health

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Background Railway suicide has profound implications for the victims and their family, and affects train drivers, railway personnel, emergency services and witnesses. To inform a multilevel prevention strategy, more knowledge is required about psychosocial and precipitating risk factors of railway suicide. Methods Data from Statistics Netherlands of all suicides between 2017 and 2021 (n = 9.241) of whom 986 died by railway suicide and interview data from a psychosocial autopsy of railway suicide decedents (n = 39) were integrated. We performed logistic regression analyses to identify sociodemographic predictors of railway suicide compared to other methods of suicide. The Constant Comparative Method was subsequently employed on interview data from the psychosocial autopsy to identify patterns in psychosocial risk factors for railway suicide. Results The strongest predictors of railway suicide compared to other suicide methods were young age (< 30 years old), native Dutch, a high educational level, living in a multi-person household (especially living with parents or in an institution), living in a rural area and a high annual household income of > 150.000 euros. Several subgroups emerged in the psychosocial autopsy interviews, which specifically reflect populations at risk of railway suicide. These subgroups were [1] young adult males with autism spectrum disorder who strived for more autonomy and an independent life, [2] young adult females with persistent suicidal thoughts and behaviours, [3] middle-aged males with a persistent mood disorder who lived with family and who faced stressors proximal to the suicide in personal and professional settings, [4] male out-of-the-blue suicides and [5] persons with psychotic symptoms and a rapid deterioration. Conclusions based on our findings we propose and discuss several recommendations to prevent railway suicide. We must continue to invest in a safe railway environment by training personnel and installing barriers. Additionally, we should adopt prevention strategies that align the needs of subgroups at increased risk, including young females who have attempted other methods of suicide and young males with autism spectrum disorder. Future research should determine the cost-effectiveness and feasibility of low-maintenance, automated interventions near crossings and psychiatric facilities.

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Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Cited by 24 publications

References 150 publications

Deep Learning in Diverse Intelligent Sensor Based Systems

Deep Learning in Diverse Intelligent Sensor Based Systems

Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video

Sociodemographic and psychosocial risk factors of railway suicide: a mixed-methods study combining data of all suicide decedents in the Netherlands with data from a psychosocial autopsy study

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