面向小运动目标的压缩域跟踪方法

压缩域跟踪是直接从压缩码流中提取运动矢量和块编码模式来实现目标对象的跟踪.针对现有压缩域跟踪方法对小运动目标跟踪性能较差的问题，本文提出了一种面向小运动目标的压缩域跟踪算法.在分析现有算法不足原因的基础上，本文从起始帧掩模的获取、离群值边界的设置和预测跟踪小目标的边缘控制三个方面提升小目标跟踪的性能，并通过数据驱动的方法寻找到块编码感知的系统参数优化.所提算法在三个小目标视频序列上进行了测试，实验结果表明，与其它压缩域跟踪算法相比，本文算法可以有效地提高小运动目标跟踪的准确率和F度量.

Compressed-Domain Object Tracking for Small Moving Targets

The compressed-domain object tracking approaches utilize the information that is directly extracted from the compressed bitstream, such as motion vector and block coding modes. Because the existing compressed-domain tracking methods have poor tracking performance for small moving targets, this study proposes a compressed-domain tracking algorithm for small moving targets. By analyzing the shortages of the existing algorithms, the performance of small target tracking is improved from the acquisition of initial frame mask, the setting of outlier boundary and the edge control of the predicating small target, and some system parameters of the block-coding system are optimized through data-driven methodology. Experiential results on three small-target video sequences show that compared with other object tracking methods, the proposed method can effectively improve the tracking performance for small moving targets in terms of accuracy and F-measure.