Drone Video Object Detection using Convolutional Neural Networks with Time Domain Motion Features
Yugui Zhang, Shen Liuqing, Xiaoyan Wang, Hai‐Miao Hu · 2020
The drone video objection detection is challenging owing to the appearance deterioration, object occlusion and motion blur in video frames, which are caused by the object motion, the camera motion, and the mixture of the object motion and the camera motion in the drone video. One of the typical solutions is to use Convolutional Neural Networks (CNNs) to train detection model by taking single frame as input. The state-of-the-art method only uses the spatial feature of the single frame in the video, but makes no use of the motion features in the time domain. In this paper, we propose a method for detecting drone video object by using convolutional neural networks in combination with time domain motion features. The proposed method includes the steps of firstly extracting the motion information between the two neighbor frames, and then combining the extracted motion information with the baseline network. In the VisDrone2019 dataset, our proposed method is shown to be effective in detecting the objects, especially in significantly reducing the occurrence of the false and missed detection of objects.