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BEVTrack: Multi-View Multi-Human Registration and Tracking in the Bird's Eye View.

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IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 24, 2025

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This study introduces BEVTrack for multi-view, multi-human tracking without camera calibration or bird's eye view (BEV) video. The novel method effectively tracks multiple subjects across camera views using spatial and temporal consistency.

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Multi-view human tracking is crucial for surveillance and robotics.
Existing methods often require camera calibration or direct bird's eye view (BEV) data, limiting real-world applicability.

Purpose of the Study:

To address the challenge of multi-view multi-human tracking in BEV without camera calibration or explicit BEV video.
To propose a novel scheme, BEVTrack, for robust tracking in complex scenarios.

Main Methods:

A virtual BEV transform module generates BEV representations from individual camera views.
A unified BEV alignment module fuses these representations using self-supervised losses for spatial and temporal consistency.
A camera-subject collaborative registration and tracking strategy leverages inter-dependencies for enhanced accuracy.

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Main Results:

The proposed BEVTrack scheme demonstrates effectiveness in multi-view multi-human tracking.
Experimental results on a newly built benchmark validate the method's performance.
The approach successfully achieves tracking without requiring camera calibration or captured BEV video.

Conclusions:

BEVTrack offers a practical solution for multi-view multi-human tracking in real-world applications.
The method's ability to track without calibration or BEV video marks a significant advancement.
The developed benchmark facilitates future research in this domain.