Existing methods of the small target detection from infrared videos are not effective with the complex background. It is mainly caused by: 1) the interference of strong edges and the similarity with other nontarget objects and 2) the lack of the context information of both the background and the target in a spatio-temporal domain. By considering these two points, we propose to slide a window in a single frame and form a spatio-temporal cube with the current frame patch and other frame patches in the spatio-temporal domain. Then, we establish a spatio-temporal tensor model based on these patches. According to the sparse prior of the target and the local correlation of the background, the separation of the target and the background can be cast as a low rank and sparse tensor decomposition problem. The target is obtained from the sparse tensor by the tensor decomposition. The experiments show that our method gains better detection performance in infrared videos with the complex background by making full use of the spatio-temporal context information.

Assessing the quality of the motion stability is important to evaluating the performance of video stabilization algorithms. This paper presents a novel quality assessment scheme for the video motion stability in a full-reference (FR) manner. Given ideally stable videos and their corresponding shaky videos, our method measures the geodesic distance between motion paths of the stable and the stabilized videos. Due to the use of the Riemannian metric defined on the manifold of spatial transformations, our method enables the intrinsic and faithful measurement on pairwise motion disparities. To facilitate the FR assessment, a data set of stable and shaky videos is constructed by directly capturing realistic stable/shaky videos with a customized device. Then, digital video stabilization algorithms can be run on shaky videos to obtain the stabilized sequence of frames, whereupon their performances are evaluated by using our stability assessment. The experiments demonstrate that our stability assessment gains good concordance with the subjective assessment.