Multi-target tracking by online learning of non-linear motion patterns and robust appearance models

@article{Yang2012MultitargetTB,
  title={Multi-target tracking by online learning of non-linear motion patterns and robust appearance models},
  author={Bo Yang and Ramakant Nevatia},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2012},
  pages={1918-1925}
}
We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous approaches that use linear motion methods only, we online build a non-linear motion map to better explain direction changes and produce more robust motion affinities between tracklets. Moreover, based on the incremental learned entry/exit map, a multiple instance learning method is devised to produce strong appearance… CONTINUE READING

Similar Papers

Figures, Results, and Topics from this paper.

Key Quantitative Results

  • We can see that our approach produces obvious improvements; fragments are greatly reduced on both data sets by over 50% and 35% respectively, while keeping other scores competitive or with some improvements.

Citations

Publications citing this paper.
SHOWING 1-10 OF 161 CITATIONS

A classification and clustering method for tracking multiple objects

  • 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)
  • 2018
VIEW 6 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

An Efficient Edge Artificial Intelligence MultiPedestrian Tracking Method With Rank Constraint

  • IEEE Transactions on Industrial Informatics
  • 2019
VIEW 6 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Human detection and tracking in surveillance videos

VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Pattern Recognition

  • Lecture Notes in Computer Science
  • 2014
VIEW 10 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2018
VIEW 7 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

A Greedy Data Association Technique for Multiple Object Tracking

  • 2017 IEEE Third International Conference on Multimedia Big Data (BigMM)
  • 2017
VIEW 10 EXCERPTS
CITES METHODS, RESULTS & BACKGROUND
HIGHLY INFLUENCED

Occlusion handling for online visual tracking using labeled random set filters

  • 2017 International Conference on Control, Automation and Information Sciences (ICCAIS)
  • 2017
VIEW 4 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2012
2019

CITATION STATISTICS

  • 28 Highly Influenced Citations

  • Averaged 21 Citations per year from 2017 through 2019

References

Publications referenced by this paper.
SHOWING 1-10 OF 22 REFERENCES

Multi-target tracking by on-line learned discriminative appearance models

  • 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 2010
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Density-aware person detection and tracking in crowds

  • 2011 International Conference on Computer Vision
  • 2011
VIEW 2 EXCERPTS

Robust unsupervised motion pattern inference from video and applications

  • 2011 International Conference on Computer Vision
  • 2011
VIEW 2 EXCERPTS