Keypoint trajectory coding on compact descriptor for video analysis

    •  Tian, D., Sun, H., Vetro, A., "Keypoint trajectory coding on compact descriptor for video analysis", IEEE International Conference on Image Processing (ICIP), DOI: 10.1109/​ICIP.2016.7532341, August 2016, pp. 171-175.
      BibTeX TR2016-127 PDF
      • @inproceedings{Tian2016aug,
      • author = {Tian, Dong and Sun, Huifang and Vetro, Anthony},
      • title = {Keypoint trajectory coding on compact descriptor for video analysis},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2016,
      • pages = {171--175},
      • month = aug,
      • doi = {10.1109/ICIP.2016.7532341},
      • issn = {2381-8549},
      • isbn = {978-1-4673-9961-6},
      • url = {}
      • }
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  • Research Area:

    Digital Video


In contrast to still image analysis, motion information offers a powerful means to analyze video. In particular, motion trajectories determined from keypoints have become very popular in recent years for a variety of video analysis tasks, including search, retrieval and classification. Additionally, cloudbased analysis of media content has been gaining momentum, so efficient communication of salient video information to perform the necessary analysis of video at the cloud server is needed. This paper describes a novel framework to efficiently represent the keypoint trajectories. In particular, an interframe prediction is designed with the option to operate in a low-delay mode. Additionally, a scalable coding method is proposed that allows for a subset of the coded trajectories in a video segment to be easily accessed. Experimental results on several popular datasets including Stanford MAR and Hopkin155 demonstrate a significant rate saving of up to 25% with our proposed trajectory coding approaches relative to a state-of-the-art reference approach.