图象序列中机动目标的形心跟踪

 从边检测边跟踪的角度探讨了图象序列中机动目标的形心跟踪问题,深入分析了强高斯噪声背景下目标形心估计的统计性质及用于形心估计的图象预处理方法。指出经典的图象二值化变换分割后作形心估计的方法面临着估计偏差和方差的矛盾,提出了用自适应交互三模型(ATIMM)跟踪图象序列中机动目标的方法,同时发现在了解目标形状的条件下,空间匹配滤波,二值变换点集聚类和 ATIMM三者的结合对图象序列中的机动目标具有最好的跟踪性能。

TRACKING THE CENTROID OF A MANEUVERING TARGET IN IMAGE SEQUENCES

This paper develops a systematic approach to track the centroid of a maneuvering target in image sequences based on the concept of Track while Detect. The statistical performance of centroid estimation with a strong Gauss noise background is studied. The authors clearly present the dilemma between performance of bias and variance when only binary transform or intensity bandpass processing is used. The suggested way for overcoming the difficulty is image pre-processing by spatial matched filtering when the shape is obtained. A simple and experimental method is found to predict the statistical performance of centroid estimation after matched filtering and segmentation. A new adaptive IMM algorithm called ATIMM is employed as the tracking filter and proved to satisfy the need of track while detect in a simulated low SNR image sequence, while the combination of matched filter, binary transform plus clustering, and ATIMM can achieve the best tracking performance.