Online multi-object tracking with yolov9 and deepsort optimized by optical flow

HIGHLIGHTS

• What: This study proposes an innovative approach to multi-object tracking by combining YOLOv9`s sophisticated detection capabilities with an enhanced DeepSORT tracking algorithm enriched through the integration of optical flow. In this context, this study proposes a new MOT approach that combines the strengths of YOLOv9 , a state-of-the-art object detector that stands out for its exceptional accuracy and efficiency in real-time object detection, with the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm , renowned for its cuttingedge approach, exploiting a synergetic relationship between the Kalman filter and the Hungarian algorithm . This model . . .
  

   

  

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