Nirikshan (Supervision)

Real Time Video Analytics Pipeline using object detection and tracking

Nirikshan aims to provide video analytics on video sources like RTSP stream or video file using deep learning models.

• Video Source
  • Video file
  • RTSP Stream
• DNN Models
  • object detection: yolo-V3/V4
  • Pose Estimation: MediaPipe
  • Face Detection
• Object Tracker: ByteTracker
• OpenCV for Video Processing and DNN for Inference
• NVIDIA Triton for Inference
• User Interface
• Python

Analytics

• Class: Person
  • Count
  • Emotion
  • Age Category
    • Young Adults
    • Middle Aged
    • Older Adults
  • Action
    • Smoking
    • Fighting
    • Patient monitoring for fall
• Class: Vehicle
  • Count
  • Type (Car, Bus, Bike)
  • Color
  • Brand
  • LPR
• Class: Animal
  • Count
  • Species

FUTURE SCOPE (Items in the list are in consideration, not finalized though)

• Action recognition in videos.
• Support for multiple video sources (IP Cameras, Local Files)
• Dockerize Analytics
• GPU Support for faster inference
• User Interface for visualizing analytics results

References/citations

• yolov3: Radmon et al. "YOLOv3: An Incremental Improvement"
• yolov4: Bochkovskiy et al. "YOLOv4: Optimal Speed and Accuracy of Object Detection"
• ByteTrack: Zhang,Yifu et al. Multi-Object Tracking by Associating Every Detection Box.
• YuNet: https://github.com/opencv/opencv_zoo/tree/main/models/face_detection_yunet