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Business Impact

  • 90%

    Object Detection Accuracy

  • 84%

    Object Classification Accuracy

  • 20-22 fps

    Rate of Stream Display

  • 6-12 fps

    Rate of Predictions from AI Engine

Customer Key Facts

  • Location : North America
  • Industry : Transportation

Real-Time Video Analytics Solution to Detect Signals, Flags, and Trespassers

The customer is a leading railway company and its locomotive crew needs to manually identify flags and signals, interpret them, and take actions according to the Canadian Rail Operating Rules. The customer lacked a video analytics solution to monitor trespassers on the railway tracks and wanted a solution to identify the concerned objects in frames faster with higher accuracy.

 

Challenges

  • Lack of sufficient training data
  • Limited hardware capabilities
  • Poor visibility conditions combined with real-time inference
  • Extreme class imbalance

Technologies Used

NVIDIA Jetson Xavier NX
Triton Inference Server

Solution

Quantiphi developed a custom IVA solution to identify trespasser hotspots and detect appropriate signals and flags. This enabled the crew to follow the standard safety guidelines effectively.

The solution was embedded with a real-time inference engine on a Rogue Carrier for NVIDIA® Jetson AGX Xavier that allowed the user to selectively switch between prediction modes (CAMS & TDS). The inference engine was a combination of custom-built object detection, classifiers, and tracking models to provide high mAP while maintaining a smaller memory footprint.

Results

  • Faster and more accurate identification of the concerned objects in the frame and quick alerting mechanism
  • Identification of trespasser hotspots enables the crew to mitigate regions with high trespassers density

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