Detection and tracking of floating objects based on spatial-temporal information fusion

Floating materials seriously damage the landscape and ecosystem of rivers and visual surveillance has become an important technique for improving the water environment. However, it remains a challenging problem in practical applications due to small-scale targets and high scene complexity with many noise problems such as water wave disturbance, light and shadow change, and strong light emission. To address these issues, this study proposes a floating object detection and tracking method based on spatial-temporal information fusion. Specifically, this study improves the network architecture of the Single Shot Multibox Detector (SSD) by enhancing the high-resolution layers to adapt to the detection task of small floating targets. Then, an improved Kernel Correlation Filter (KCF) by introducing a fast histogram of oriented gradient (FHOG) and a pyramid scale estimation strategy is proposed to achieve the estimation of the position and size of floating objects. More significantly, a spatial-temporal information fusion strategy is applied to complement detection information with tracking information based on feature comparison. The proposed method is trained and compared with the stateof-the-art methods based on multiple scenarios. The results show that the proposed method has better performance than other methods in different scenarios, and achieves an average accuracy of more than 91% with a speed of 15.55 FPS, which prove that our method can well complete the detection and tracking task of floating objects. This work enriches the framework of "tracking by detection" and extends the application of floating object detection and tracking in surface vision.