Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation
Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks....
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8102 |
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| author | Johan Barthelemy Umair Iqbal Yan Qian Mehrdad Amirghasemi Pascal Perez |
| author_facet | Johan Barthelemy Umair Iqbal Yan Qian Mehrdad Amirghasemi Pascal Perez |
| author_sort | Johan Barthelemy |
| collection | DOAJ |
| description | Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks. To address this issue, we propose an advanced artificial intelligence (AI) solution for identifying unsafe behaviours in public transport. The proposed approach employs deep learning action recognition models and utilises technologies like NVIDIA DeepStream SDK, Amazon Web Services (AWS) DirectConnect, local edge computing server, ONNXRuntime and MQTT to accelerate the end-to-end pipeline. The solution captures video streams from remote train stations closed circuit television (CCTV) networks, processes the data in the cloud, applies the action recognition model, and transmits the results to a live web application. A temporal pyramid network (TPN) action recognition model was trained on a newly curated video dataset mixing open-source resources and live simulated trials to identify the unsafe behaviours. The base model was able to achieve a validation accuracy of 93% when trained using open-source dataset samples and was improved to 97% when live simulated dataset was included during the training. The developed AI system was deployed at Wollongong Train Station (NSW, Australia) and showcased impressive accuracy in detecting violence incidents during an 8-week test period, achieving a reliable false-positive (FP) rate of 23%. While the AI correctly identified 30 true-positive incidents, there were 6 cases of false negatives (FNs) where violence incidents were missed during the rainy weather suggesting more data in the training dataset related to bad weather. The AI model’s continuous retraining capability ensures its adaptability to various real-world scenarios, making it a valuable tool for enhancing safety and the overall passenger experience in public transport settings. |
| format | Article |
| id | doaj-art-ca0cf8d9f9dd4635bb743555a7c9cd6f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ca0cf8d9f9dd4635bb743555a7c9cd6f2024-12-27T14:52:59ZengMDPI AGSensors1424-82202024-12-012424810210.3390/s24248102Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public TransportationJohan Barthelemy0Umair Iqbal1Yan Qian2Mehrdad Amirghasemi3Pascal Perez4Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, AustraliaCentre for Geotechnical Science and Engineering, School of Engineering, University of Newcastle, Newcastle, NSW 2308, AustraliaFaculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, AustraliaFaculty of Business and Law, University of Wollongong, Wollongong, NSW 2522, AustraliaAustralian Urban Research Infrastructure Network (AURIN), University of Melbourne, Melbourne, VIC 3052, AustraliaPublic transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks. To address this issue, we propose an advanced artificial intelligence (AI) solution for identifying unsafe behaviours in public transport. The proposed approach employs deep learning action recognition models and utilises technologies like NVIDIA DeepStream SDK, Amazon Web Services (AWS) DirectConnect, local edge computing server, ONNXRuntime and MQTT to accelerate the end-to-end pipeline. The solution captures video streams from remote train stations closed circuit television (CCTV) networks, processes the data in the cloud, applies the action recognition model, and transmits the results to a live web application. A temporal pyramid network (TPN) action recognition model was trained on a newly curated video dataset mixing open-source resources and live simulated trials to identify the unsafe behaviours. The base model was able to achieve a validation accuracy of 93% when trained using open-source dataset samples and was improved to 97% when live simulated dataset was included during the training. The developed AI system was deployed at Wollongong Train Station (NSW, Australia) and showcased impressive accuracy in detecting violence incidents during an 8-week test period, achieving a reliable false-positive (FP) rate of 23%. While the AI correctly identified 30 true-positive incidents, there were 6 cases of false negatives (FNs) where violence incidents were missed during the rainy weather suggesting more data in the training dataset related to bad weather. The AI model’s continuous retraining capability ensures its adaptability to various real-world scenarios, making it a valuable tool for enhancing safety and the overall passenger experience in public transport settings.https://www.mdpi.com/1424-8220/24/24/8102safetypublic transportartificial intelligence (AI)action recognitioncomputer vision |
| spellingShingle | Johan Barthelemy Umair Iqbal Yan Qian Mehrdad Amirghasemi Pascal Perez Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation Sensors safety public transport artificial intelligence (AI) action recognition computer vision |
| title | Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation |
| title_full | Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation |
| title_fullStr | Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation |
| title_full_unstemmed | Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation |
| title_short | Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation |
| title_sort | safety after dark a privacy compliant and real time edge computing intelligent video analytics for safer public transportation |
| topic | safety public transport artificial intelligence (AI) action recognition computer vision |
| url | https://www.mdpi.com/1424-8220/24/24/8102 |
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