A Case Study of Object Recognition from Drone Videos
A Case Study of Object Recognition from Drone Videos
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无人机视频对象识别案例研究
DOI：
10.1109/icict52872.2021.00021
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
J. J. Li
中科院分区：
文献类型：
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作者：
Stacy Fortes;R. Kulesza;J. J. Li
为了研究潜在的自主无人机的对象识别和反应，我们创建了一个卷积神经网络(CNN)，并用它来检测和统计从无人机视频片段拍摄的停车场中的空停车点。我们首先通过监督学习对网络进行训练，使用之前无人机拍摄的单个停车点的快照，正确地将停车点分类为空的或有人的。然后我们存储该模型，用于在新的无人机视频中检测和标记对象，例如空置和占用的地点，以及进出地点的汽车。我们发明了一种视频对象参考(VOR)来估计对象的尺寸。经过多轮调整，我们最终达到了接近100%的准确率。我们的结论是，调整批次大小和历元数可以提高目标识别能力。我们希望这项研究将有助于调整CNN对无人机视频的对象识别，以帮助最终的自主无人机。
To study a potential autonomous drone's object recognition and reaction, we created a convolutional neural network (CNN) and used it to detect and count the empty parking spots in a parking lot taken from drone video footage. We first trained the network through supervised learning with snapshots of individual parking spots, from a previous drone footage, to correctly classify the spots as empty or occupied. Then we store the model to be used for detection and labeling of objects in new drone videos such as empty vs. occupied spots, as well as cars moving in and out of spots. We invented a video object referencing (VOR) to estimate object dimensions. After many rounds of tuning, we eventually achieve close to a hundred percent of accuracy. We concluded that adjusting batch size and epoch number could improve object recognition. We hope this research will contribute to tuning CNN for object recognition from drone videos to help with eventual autonomous drones.