Object Detection and Trcacking Based on Convolutional Neural Networks for High-Resolution Optical Remote Sensing Video

Object detection algorithms, from high-resolution optical remote sensing images, have been booming from the last few years. However, object tracking for high-resolution optical remote sensing video is a challenging task due to the large number and small size of objects. In this paper, we propose an object detection and tracking method based on deep convolutional neural networks for wide swath high-resolution optical remote sensing videos. The proposed method firstly segments each frame of a video into sub-samples using a sliding window of fixed size. In order to detect the objects appearing at the edge of the sliding window efficiently, we use an overlapping sliding window sampling method. Further, we design a network fusing region of interests (RoIs) of the previous and current frames to track the objects occurred in the previous frames of the video. RoIs of previous frame are applied directly to the feature layer of the current frame. Finally, for each frame, we merge the detection and tracking results of sub-samples by non-maximum suppression (NMS) method. The experimental results on our dataset demonstrate the validity and generality of the proposed detection algorithm.