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Efficient Online Vehicle Tracking for Real—Virtual Mapping Systems

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Abstract

Multi-object tracking is a vital problem as many applications require better tracking approaches. Although learning-based detectors are becoming extremely powerful, there are few tracking methods designed to work with them in real time. We explored an efficient flexible online vehicle tracking-by-detection framework suitable for real-virtual mapping systems, which combines a non-recursive temporal window search with delayed output and produces stable trajectories despite noisy detection responses. Its computation speed meets the real-time requirements, whereas its performance is comparable with that of state-of-the-art online trackers on the DETRAC dataset. The trajectories from our approach also contain the target class and color information important for virtual vehicle motion reconstruction.

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Abbreviations

A, B :

Bounding boxes

c :

Center point of bounding box

d A∪B :

Diagonal length of the minimum rectangle area containing A and B

p, ṗ :

Image-space position and velocity vectors

P, V :

World-space position and velocity vectors

s :

Score of confidence

t :

Time, frame

w :

Weight

x :

Feature vector

α, β :

Low and high thresholds

ε :

Small quantity

Σ :

Covariance matrix

σ 2 :

Variance

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Authors and Affiliations

  1. School of Software, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jiacheng Chen  (陈佳诚) & Xubo Yang  (杨旭波)

  2. Shanghai International Automobile City (Group) Co., Ltd., Shanghai, 201805, China

    Lin Li  (李霖)

Authors
  1. Jiacheng Chen  (陈佳诚)
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  2. Lin Li  (李霖)
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  3. Xubo Yang  (杨旭波)
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Corresponding author

Correspondence to Jiacheng Chen  (陈佳诚).

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Chen, J., Li, L. & Yang, X. Efficient Online Vehicle Tracking for Real—Virtual Mapping Systems. J. Shanghai Jiaotong Univ. (Sci.) 26, 598–606 (2021). https://doi.org/10.1007/s12204-021-2349-6

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  • DOI: https://doi.org/10.1007/s12204-021-2349-6

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