DETECTING SMALL, MOVING-OBJECTS IN IMAGE SEQUENCES USING SEQUENTIAL HYPOTHESIS-TESTING

被引:158
作者
BLOSTEIN, SD [1 ]
HUANG, TS [1 ]
机构
[1] UNIV ILLINOIS,COLL ENGN,COORDINATED SCI LAB,URBANA,IL 61801
关键词
TRACKING; TARGETS; ALGORITHM; EFFICIENCIES;
D O I
10.1109/78.134399
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A new algorithm is proposed for the solution of an important class of multidimensional detection problems: the detection of small, barely discernible, moving objects of unknown position and velocity in a sequence of digital images. A large number of candidate trajectories, organized into a tree structure, are hypothesized at each pixel in the sequence and tested sequentially for a shift in mean intensity. The practicality of the algorithm is facilitated by the use of multistage hypothesis testing (MHT) for simultaneous inference, as well as the existence of exact expressions for MHT test performance in Gaussian white noise (GWN). These expressions predict the algorithm's computation and memory requirements, where it is shown theoretically that several orders of magnitude of processing are saved over a brute-force approach based on fixed sample-size tests. The algorithm is applied to real data by using a robust preprocessing procedure to eliminate background structure and transform the image sequence into a residual representation, modeled as GWN. Results are verified experimentally on a variety of video image sequences.
引用
收藏
页码:1611 / 1629
页数:19
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