Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

@article{He2019TrackingBA,
  title={Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers},
  author={Zhen He and Jian Li and Daxue Liu and Hangen He and David Barber},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={1318-1327}
}
  • Zhen He, Jian Li, D. Barber
  • Published 10 September 2018
  • Computer Science
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However, the commonly used supervised learning approaches require the labeled data (e.g., bounding boxes), which is expensive for videos. Also, the TBD… 

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References

SHOWING 1-10 OF 90 REFERENCES

Learning to Track: Online Multi-object Tracking by Decision Making

TLDR
This work forms the online MOT problem as decision making in Markov Decision Processes (MDPs), where the lifetime of an object is modeled with a MDP, and a similarity function for data association is equivalent to learning a policy for the MDP.

Visual Tracking with Fully Convolutional Networks

TLDR
An in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet shows that the proposed tacker outperforms the state-of-the-art significantly.

Non-Markovian Globally Consistent Multi-object Tracking

TLDR
This paper proposes a non-Markovian approach to imposing global consistency by using behavioral patterns to guide the tracking algorithm, and shows significant improvements both in supervised settings where ground truth is available and behavioral patterns can be learned from it, and in completely unsupervised settings.

Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera

TLDR
This paper proposes a novel approach for multiperson tracking-by-detection in a particle filtering framework that detects and tracks a large number of dynamically moving people in complex scenes with occlusions, requires no camera or ground plane calibration, and only makes use of information from the past.

Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

  • Bo WuR. Nevatia
  • Computer Science
    International Journal of Computer Vision
  • 2006
TLDR
This work presents an approach to automatically detect and track multiple, possibly partially occluded humans in a walking or standing pose from a single camera, which may be stationary or moving.

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

TLDR
OLDAMs have significantly higher discrimination between different targets than conventional holistic color histograms, and when integrated into a hierarchical association framework, they help improve the tracking accuracy, particularly reducing the false alarms and identity switches.

People-tracking-by-detection and people-detection-by-tracking

TLDR
This paper combines the advantages of both detection and tracking in a single framework using a hierarchical Gaussian process latent variable model (hGPLVM) and presents experimental results that demonstrate how this allows to detect and track multiple people in cluttered scenes with reoccurring occlusions.

Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions

TLDR
This paper shows how to efficiently handle splitting and merging during track linking, and shows that the identities of objects that merge together and subsequently split can be maintained, which enables the identity of objects to be maintained throughout long sequences with difficult conditions.

Multiple Object Tracking Using K-Shortest Paths Optimization

TLDR
This paper shows that reformulating that step as a constrained flow optimization results in a convex problem and takes advantage of its particular structure to solve it using the k-shortest paths algorithm, which is very fast.

Robust Object Tracking by Hierarchical Association of Detection Responses

TLDR
This work presents a detection-based three-level hierarchical association approach to robustly track multiple objects in crowded environments from a single camera and shows a great improvement in performance compared to previous methods.
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