基于Camshift的多特征自适应融合船舶跟踪算法

Camshift Ship Tracking Algorithm Based on Multi-feature Adaptive Fusion

基于航道内船舶监控图像序列的多目标跟踪技术是开展船撞主动预警,提升桥区船舶通航安全的前提。基于颜色直方图的Camshift跟踪算法在复杂气象条件下无法得到准确的跟踪结果,本文提出了一种多特征自适应融合的多目标跟踪算法。该算法的目标模型由颜色、形状及纹理多特征自适应融合实现,增加了描述目标模型的可靠性和鲁棒性;在跟踪目标时,将融合信息目标模型结合到Camshift跟踪算法中。实验结果表明,该算法与传统Camshift跟踪算法相比,具有更高的准确性和可靠性。

Multi-target tracking based on vessel image sequences monitored on waterway is the premise of carrying out the vessel active warning and improving bridge-area ship navigation safety. Accurate tracking results are unable to be obtained by traditional Camshift tracking only based on color histogram under complex weather conditions. For this reason, a Camshift ship tracking algorithm based on color, shape and texture multi-feature adaptive fusion is proposed. In this method, the target model is realized by multi-feature adaptive fusion, which increases the reliability of observation and improves the robustness of observation model. When tracking targets, the target model based on fusion is included in the Camshift tracking algorithm. The experimental results show that the proposed tracking algorithm can provide higher correctness and stability than traditional Camshift algorithm.