Multiple-joint pedestrian tracking using periodic models

Marzieh Dolatabadi (Corresponding author), Jos Elfring, René van de Molengraft

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)


Estimating accurate positions of multiple pedestrians is a critical task in robotics and autonomous cars. We propose a tracker based on typical human motion patterns to track multiple pedestrians. This paper assumes that the legs’ reflection and extension angles are approximately changing periodically during human motion. A Fourier series is fitted in order to describe the moving, such as describing the position and velocity of the hip, knee, and ankle. Our tracker receives the position of the ankle, knee, and hip as measurements. As a proof of concept, we compare our tracker with state-of-the-art methods. The proposed models have been validated by experimental data, the Human Gait Database (HuGaDB), and the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) tracking benchmark. The results indicate that our tracker is able to estimate the reflection and extension angles with a precision of 90.97%. Moreover, the comparison shows that the tracking precision increases up to 1.3% with the proposed tracker when compared to a constant velocity based tracker.

Original languageEnglish
Article number6917
Number of pages17
Issue number23
Publication statusPublished - 1 Dec 2020


Funding: This work was supported by the EU Horizon 2020 Program under Grant Agreements No. 731993 AUTOPILOT (Automated Driving Progressed by Internet Of Things) project. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors.

FundersFunder number
European Commission
Horizon 2020731993


    • Harmonic motion
    • Joint tracking
    • Kinematics estimation
    • Pedestrian–car interaction
    • Tracking algorithm


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