光学学报, 2017, 37 (12): 1215006, 网络出版: 2018-09-06   

基于多域卷积神经网络与自回归模型的空中小目标自适应跟踪方法 下载: 995次

Adaptive Tracking Algorithm for Aerial Small Targets Based on Multi-Domain Convolutional Neural Networks and Autoregression Model
作者单位
1 中北大学计算机与控制工程学院, 山西 太原 030051
2 酒泉卫星发射中心, 甘肃 酒泉 735000
摘要
针对星空背景下卫星跟踪中运动小目标与伪目标交会造成的跟踪漂移问题,提出一种基于多域卷积神经网络(MDNet)与自回归(AR)模型的空中小目标自适应跟踪方法。 对用MDNet采集到的图像序列第1帧的正样本进行bounding-box回归模型训练;再训练用最小信息准则和最小二乘法确定阶数和参数的AR模型,估计目标运动轨迹并预测目标位置;最后,将该目标位置作为MDNet的采样中心,约束采样候选区域,用bounding-box回归模型调整目标位置。 实验用8种跟踪方法测试了8组场景复杂的视频序列,结果表明,本文方法的成功率及平均覆盖率均显著高于其他7种典型算法,具有较高的精确性和稳健性。
Abstract
The adaptive tracking algorithm for the aerial small target is proposed based on the multi-domain convolutional neural networks (MDNet)and the autoregression (AR) model,to solve the tracking drift problem that the pseudo targets and the small target converge in the sky background. Firstly, the positive samples of the first frame in the image sequence are collected by MDNet to train the bounding-box regression model. Secondly, the AR model with its order and parameters determined by the Akaike information criterion and least squares method is trained to estimate the target track and to predict the target position. Finally, the region of sampling candidate is constrained since MDNet collects samples centered on the predicted target location, and then the target position is adjusted by the bounding-box regression model. Eight groups of benchmark video sequences are tested with the proposed algorithm and another seven classical tracking algorithms, and obtained results are compared. The experimental results show that the success rate and the average overlap rate of the proposed adaptive tracking algorithm are higher than those of other algorithms, and the proposed algorithm has higher accuracy and robustness.

蔺素珍, 郑瑶, 禄晓飞, 曾建潮. 基于多域卷积神经网络与自回归模型的空中小目标自适应跟踪方法[J]. 光学学报, 2017, 37(12): 1215006. Suzhen Lin, Yao Zheng, Xiaofei Lu, Jianchao Zeng. Adaptive Tracking Algorithm for Aerial Small Targets Based on Multi-Domain Convolutional Neural Networks and Autoregression Model[J]. Acta Optica Sinica, 2017, 37(12): 1215006.

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