Rotated vehicle object detection and tracking on drone-captured scenarios

Minglong Zhou; Ning Li; Lize Miao; Huiyu Zhou

doi:10.1117/12.2684333

1 August 2023 Rotated vehicle object detection and tracking on drone-captured scenarios

Minglong Zhou, Ning Li, Lize Miao, Huiyu Zhou

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 1275438 (2023) https://doi.org/10.1117/12.2684333
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

The vehicle object detection and tracking on drone-captured scenarios will play a significant role in the intelligent transportation in future. In order to provide accurate orientation and scale information, the angle vector module is introduced to improve the model to realize the rotated detection. For the small and densely distributed objects, the C3_TR structure is proposed to improve the detection accuracy. The image weight and the category weight are used in training to improve accuracy on the self-built vehicle dataset with the category imbalance. Finally, the mAP of the improved YOLOv5 detector is 82.4% higher than the baseline model by 8.1 percentage points. The angle parameter is taken into account and the rotated IOU matching is used instead of the horizontal IOU matching to realize the tracking with rotated bounding boxes by DeepSORT. In tracking evaluation results on the self-built dataset, the HOTA, MOTA, MOTP and ID_SW are 76.86%, 92.8%, 81.3% and 2, respectively.

Citation Download Citation

Minglong Zhou, Ning Li, Lize Miao, and Huiyu Zhou "Rotated vehicle object detection and tracking on drone-captured scenarios", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 1275438 (1 August 2023); https://doi.org/10.1117/12.2684333

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KEYWORDS

Object detection

Detection and tracking algorithms

Signal filtering

Video

Education and training

Covariance matrices

Transformers

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