Track and detection for specified moving object based on Convolutional Neural Networks
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    Abstract:

    The detection and track of specified moving small object is an important subject in Computer Vision. By changing the position of the feature maps for fusion in the YOLOv3, building the custom database including three classes, and completing the combination of classes by using Intersection Over Union(IOU), a detector is created, which is able to detect the specified moving small object and makes mAP@75 reach 47.41 in the test customer's data set. Combining Kalman Filter and Hungarian method, and putting the scale information of predicted bounding box and ground bounding box, the detector can track the object and reduce the ID switch caused by camera's fast movement. The whole system's speed reaches up to 0.109 7 s/frame using one NVIDIA GeForce GTX 1060 6GB GPU.

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陈 斌,王 磊.基于一种卷积神经网络的目标快速跟踪技术[J]. Journal of Terahertz Science and Electronic Information Technology ,2021,19(5):922~928

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History
  • Received:December 24,2019
  • Revised:April 02,2020
  • Adopted:
  • Online: November 01,2021
  • Published: