基于深度学习的多船舶目标跟踪与流量统计

研究船舶的目标跟踪对提高水上目标视频图像智能监管的水平有着至关重要的作用,系统通过深度学习SSD模型进行对船舶目标定位检测,使用修正的KCF算法对检测到的船舶目标进行跟踪。把深度学习的方式引入船舶目标检测领域,与传统检测方法相比精准率大大提高,同时提出了一个修正的KCF算法对多船舶目标进行跟踪,较好地解决了目标漏检与重复统计的问题。对大量船舶目标样本进行训练学习后,船舶检测定位精准,检测成功率达到91%以上,船舶跟踪算法快速稳定,检测与跟踪算法达到30帧每秒,船舶目标流量统计准确率达到95%以上,整个系统框架满足实时性的要求。

Multi-ship Target Tracking and Flow Statistics Based on Deep Learning

The research on ship tracking from video surveillance plays a vital role in improving the intelligence of maritime safety regulation.We applied a deep learning method-SSD to detect and locate ship targets,then proposed a modified KCF algorithm to track and count these targets.By introducing deep learning methods,the accuracy of ship detection is greatly improved comparing with the traditional methods.An improved KCF algorithm can realize to track more than one ships.It solves the problem of ship detection and statistics and without missing.Benefits from the modified KCF algorithm,omission and duplication failure are effectively relieved while maintaining real-time processing speed.According to the experiments we performed,the accuracy of traffic flow data reaches 95%.