基于时空信息融合的无人艇水面目标检测跟踪

Object detection and tracking is essential in the navigation, obstacle avoidance and other tasks of Unmanned Surface Vehicles (USV). However, the environment on the water is complex, and there are many problems such as object scale variation, occlusion, illumination variation and camera shaking, etc. This paper proposes the visual object detection and tracking of USV based on spatial-temporal information fusion. Deep learning detection in space is used to extract single-frame depth semantic features and correlation filter tracking in time is used to calculate the correlation of oriented gradient feature between frames. Temporal and spatial information through feature comparison are combined to achieve continuous and stable object detection and tracking with strong robustness at real-time. The experiments results demonstrate that the average detection and tracking accuracy is 0.83 with the average running speed of 15 fps, which illustrates the accuracy is improved and the speed is high.