AI Platform Incident Detector and Video Surveillance System
Safety for passengers in railway stations and on platforms is paramount. These days, there are many cameras in railway stations for monitoring passengers to keep them safe and to protect secure areas. If an emergency occurs, station operators must be able to identify incidents immediately and deal with them quickly.
Traditional video surveillance systems
utilize video technology to automatically detect incidents on the platform and
in the station. However, these traditional systems suffer from relatively low quality
video and accurate analytics. What’s more, it takes too much time and effort
for station operators to recognize and acknowledge false alarms. Today though,
using AI (Artificial Intelligence) trained models, people can be easily detected
and tracked, and most false alarms caused by non-human artifacts and objects
can be distinguished and eliminated from the data. AI makes it possible to
raise detection rate accuracy significantly and greatly improve passenger
Typical train stations need multiple cameras to cover the entire length of track along the platform. For this project, each camera conducts surveillance in its own preset monitoring area to detect whether objects have fallen from the platform onto the track. The system needed to handle several cameras and be able to detect people from inanimate objects using AI deep learning and training methods. Powerful edge-based AI systems that leveraged GPU cards were deployed along the platform to detect incidents.
Apart from the
platform incident detection system, all cameras in the railway station are
monitored by the AI inference system in the center control room. If an incident
occurs in any security zone in the station, staff have to be able to control
the situation quickly. In order to deal quickly with incidents, backend systems
need to trigger alarm messages to notify station staff and even train drivers. The
system also has to control related systems like railway signaling, and warning alarms
with announcements to make the whole station aware of the incident. The system
is expected to reduce the burden on station employees since most railway
operators these days suffer staff shortages but passenger safety cannot be sacrificed.
In the central
control room, the AI inference server monitors all cameras in the station. To
process all this massive amount of information, multiple NVIDIA GPU cards were
integrated into a single server. SKY-6400 4U rackmount server supports up to 6 x
NVIDIA GPU cards, including 4 x PCIe x16 double-deck cards, 1 x PCIe x8, and 1 x
PCIe x4 used for heavy AI deep learning processing. Thermal issues are always a
concern when GPU cards are used, so SKY-6000 series servers have an industrial
design and are NVQual certified to guarantee AI computing performance. The solution
provided a highly reliable AI video monitoring system for passenger and railway
- Full range of products from AI server to edge AI system end-to-end solution.
- Industrial edge AI system empowers AI computing at the road-side with minimum deployment effort.
- Deep learning computing at the back-end permits self-adaptive traffic light control.