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<p>Urban traffic areas, such as intersections, significantly escalate the risk of accidents due to the high density and diverse behaviour of road users. Achieving higher automation levels necessitates robust object recognition and tracking capabilities to mitigate accident risk. Modern vehicle architectures utilize multiple sensor systems to ensure robustness and high-quality perception. For camera systems in particular, multi-camera multi-target tracking (MCMTT) with visual re-identification (ReID) is fundamental for understanding object behaviour across different fields of view. This work directly addresses this need by presenting and evaluating a deep learning-based concept for near-field MCMTT using Waymo’s real-world perception dataset [<CitationRef CitationID="CR1">1</CitationRef>]. This work enables comprehensive and reliable tracking, focusing on the investigation of concept parameters and highlighting the essential factors for the automated driving task.</p>

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ReID-based multi-camera multi-target tracking concept for automated driving in urban environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Marius Westendorf,
  • Jonas Brinkmann,
  • Marcel Kascha,
  • Maximilian Flormann,
  • Roman Henze

摘要

Urban traffic areas, such as intersections, significantly escalate the risk of accidents due to the high density and diverse behaviour of road users. Achieving higher automation levels necessitates robust object recognition and tracking capabilities to mitigate accident risk. Modern vehicle architectures utilize multiple sensor systems to ensure robustness and high-quality perception. For camera systems in particular, multi-camera multi-target tracking (MCMTT) with visual re-identification (ReID) is fundamental for understanding object behaviour across different fields of view. This work directly addresses this need by presenting and evaluating a deep learning-based concept for near-field MCMTT using Waymo’s real-world perception dataset [1]. This work enables comprehensive and reliable tracking, focusing on the investigation of concept parameters and highlighting the essential factors for the automated driving task.