Multi‐label learning based target detecting from multi‐frame data - Mengqing Mei (Wiley, 2021)
Abstract In the field of target detecting, lots of progress have been made in recent years. Owing to the progress of multiple frames time series data, or video satellites, target detecting from space‐borne satellite videos has been available. However, detecting a slightly moving target from space‐borne videos is still a difficult task, because of the low resolution and illumination variation influence. This paper considers target detecting from time series data as multi‐label problem as there are several different kinds of background objects and targets of interest. To some extent the background of time series data is comparative invariant, using background analysis method to extract the target from the background is promising. This paper proposes a novel target detecting algorithm based on multi‐label learning and Gaussian background description model aiming at extracting slowly moving target. To further enhance performances, multi‐frame fusion and post processing method was utilized to catch the slight difference due to movement. Experimental results on real world datasets indicate that the proposed method outperforms some state‐of‐the‐art algorithms.