Multi-Object Detection and Tracking on Mobile Device Based on DeepLearning

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Other Title
  • DeepLearningによるモバイル端末での多目標検出と追跡


<p>Deep learning promotes the development of computer vision, but like R-CNN series models with accurate advantage and YOLO series models with speed advantage, which are heavily dependent on the powerful GPU. So it is difficult to deploy on mobile applications to achieve higher performance in mobile vision. In this study, we used the light weight MobileNet as the backbone to extract objects feature maps and referred to the training strategy of SSD and YOLO to achieve real-time multi-object detection. In addition, we carried out the multi-object tracking after detection automatically through processing some cached information like feature maps and motion position in a sequence of frames. Our proposed detection and tracking model achieved 80 categories detection at the speed of 12 FPS and up to 16 objects tracking simultaneously at the speed of over 30 FPS on iPhone 8.</p>



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