mlsanityData to 7 Oct 2026

Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring

arXiv 2410.13616 ↗First posted 17 Oct 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

This work presents advancements in multi-class vehicle detection using UAV cameras through the development of spatiotemporal object detection models. The study introduces a Spatio-Temporal Vehicle Detection Dataset (STVD) containing 6, 600 annotated sequential frame images captured by UAVs, enabling comprehensive training and evaluation of algorithms for holistic spatiotemporal perception. A YOLO-based object detection algorithm is enhanced to incorporate temporal dynamics, resulting in improved performance over single frame models. The integration of attention mechanisms into spatiotemporal models is shown to further enhance performance. Experimental validation demonstrates significant progress, with the best spatiotemporal model exhibiting a 16.22% improvement over single frame models, while it is demonstrated that attention mechanisms hold the potential for additional performance gains.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 44 of its 63 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

44 of 63 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directionscited by 0