• Corpus ID: 215786215

Multi-Object Tracking with Siamese Track-RCNN

  title={Multi-Object Tracking with Siamese Track-RCNN},
  author={Bing Shuai and Andrew G. Berneshawi and Davide Modolo and Joseph Tighe},
Multi-object tracking systems often consist of a combination of a detector, a short term linker, a re-identification feature extractor and a solver that takes the output from these separate components and makes a final prediction. Differently, this work aims to unify all these in a single tracking system. Towards this, we propose Siamese Track-RCNN, a two stage detect-and-track framework which consists of three functional branches: (1) the detection branch localizes object instances; (2) the… 

Application of Multi-Object Tracking with Siamese Track-RCNN to the Human in Events Dataset

This work proposes Siamese Track-RCNN, a two stage detect-and-track framework which consists of three functional branches: (1) the detection branch localizes object instances; (2) theSiamese-based track branch estimates the object motion and (3) the object re-identification branch re-activates the previously terminated tracks when they re-emerge.

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SynDHN: Multi-Object Fish Tracker Trained on Synthetic Underwater Videos

  • M. A. MartijaP. Naval
  • Computer Science
    2020 25th International Conference on Pattern Recognition (ICPR)
  • 2021
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A new instance-to-track matching objective to learn appearance embedding that compares a candidate detection to the embedding of the tracks persisted in the tracker is designed that enables us to learn not only from videos labeled with complete tracks, but also unlabeled or partially labeled videos.

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City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

  • Chong LiuYuqi Zhang Yiyan Shen
  • Computer Science
    2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2021
The solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21) is described and the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed.

Self-Supervised Small Soccer Player Detection and Tracking

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Fast Online Object Tracking and Segmentation: A Unifying Approach

This method improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task, and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second.

Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

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