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首页> 外文会议>IEEE World Symposium on Applied Machine Intelligence and Informatics >Implementation of Target Tracking Methods on Images Taken from Unmanned Aerial Vehicles
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Implementation of Target Tracking Methods on Images Taken from Unmanned Aerial Vehicles

机译：目标跟踪方法在无人机图像上的实现

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Traditional object detection algorithms generate proposals and implement feature extraction. Then, a classification algorithm is implemented to label object classes. This process is slow, and the accuracy may not be adequate for UAV's real-time application tasks due to their movement in the air. We specified and practically implemented an object detection and localization scheme on images taken from a UAV, and provided the UAV with an advanced vision. We used YOLOv2 model. The YOLOv2 is a suitable object detection approach based on deep learning, and it presents a network architecture with accurate results in high speed. The object detection and localization were successfully implemented for people, car, and motorcycle classes within the threshold confidence scores. We pre-trained the model on COCO dataset and tested the model with our test images. The confidence scores were higher in altitudes from 5 to 15 meters and the confidence scores varied between %45 - %79 mAP.
机译：传统的对象检测算法会生成建议并实现特征提取。然后，实施分类算法以标记对象类别。这个过程很慢，由于无人机在空中移动，其准确性可能不足以满足无人机的实时应用任务。我们为从无人机获取的图像指定并实际实施了对象检测和定位方案，并为无人机提供了先进的视野。我们使用了YOLOv2模型。 YOLOv2是基于深度学习的一种合适的对象检测方法，它提出了一种网络结构，具有高速，准确的结果。在阈值置信度得分内，已成功地对人，汽车和摩托车类实施了对象检测和定位。我们在COCO数据集上对模型进行了预训练，并使用我们的测试图像对模型进行了测试。在5至15米的海拔高度上，置信度得分较高，置信度得分在％45-％79 mAP之间变化。

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