• Corpus ID: 215786559

ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking

@article{Saleh2020ArTISTAT,
  title={ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking},
  author={Fatemeh Sadat Saleh and Mohammad Sadegh Aliakbarian and Mathieu Salzmann and Stephen Gould},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.07482}
}
One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets, typically done via a scoring function. Despite the great advances in MOT, designing a reliable scoring function remains a challenge. In this paper, we introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion. One key property of our model is its ability to… 

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