Target tracking is an important area of research in computer vision where stable target's tracking has been well solved. But in real world, it is difficult to ensure that the camera or lens could be fixed and the target could maintain its shape in whole video sequence. And as a result, in these unstable cases, robust tracking algorithms have to deal with the problem of target shape-deforming. Once the scenes video sequence contains shape-deformed target, tracking become a real challenging problem. Most previous tracking algorithms based on craft features only used HOG or/and CN features. This paper proposed an algorithm named as Correlation Filtering with Motion Detection (CFMD). This algorithm takes into account the camera shake and target motion information of the video sequence. After removing the effects of lens shake and camera movement, this algorithm can predict the motion information of the target, thereby effectively improving the tracking accuracy and robustness. In CFMD, the target position is determined by the weighted outputs of motion detection and correlation filter tracker. We evaluated our CMFD algorithm on the OTB-100 and VOT-2018 dataset compared with other target tracking algorithms, including Kernel Correlation Filter (KCF), Scale Adaptive with Multiple Features tracker (SAMF), Discriminative Scale Space Tracker (DSST), and Sum of Template and Pixel-wise LEarners (Staple), Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking(STRCF), Multi-Cue Correlation Filters for Robust Visual Tracking(MCCT). The experimental results showed that our algorithm owns the property of robust tracking of shape-deformed targets in video sequences containing lens shaking or camera moving and it achieves the state-of-the-art precision and tracking effects. INDEX TERMS Robust target tracking, shape-deformed target, correlation filter, motion detection.

Although in-orbit anomaly detection is extremely important to ensure spacecraft safety, the complex spatial-temporal correlation and sparsity of anomalies in the data pose significant challenges. This study proposes the new multi-task learning-based time series anomaly detection (MTAD) method, which captures the spatial-temporal correlation of the data to learn the generalized normal patterns and hence facilitates anomaly detection. First, four proxy tasks are implemented for feature extraction through joint learning: (1) Long short-term memory-based data prediction; (2) autoencoder-based latent representation learning and data reconstruction; (3) variational autoencoder-based latent representation learning and data reconstruction; and (4) joint latent representation-based data prediction. Proxy Tasks 1 and 4 capture the temporal correlation of the data by fusing the latent space, whereas Tasks 2 and 3 fully capture the spatial correlation of the data. The isolation forest algorithm then detects anomalies from the extracted features. Application to a real spacecraft dataset reveals the superiority of our method over existing techniques, and further ablation testing for each task proves the effectiveness of fusing multiple tasks. The proposed MTAD method demonstrates promising potential for effective in-orbit anomaly detection for spacecraft.

Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel Contour Optimized Non-Iterative Clustering (CONIC) method is presented. It incorporates contour prior into the non-iterative clustering framework, aiming to provide a balanced trade-off between segmentation accuracy and visual uniformity. After the conventional grid sampling initialization, a regional inter-seed correlation is first established by the joint color-spatial-contour distance. It then guides a global redistribution of all seeds to modify the number and positions iteratively. This is done to avoid clustering falling into the local optimum and achieve the exact number of user-expectation. During the clustering process, an improved feature distance is elaborated to measure the color similarity that considers contour constraint and prevents the boundary pixels from being wrongly assigned. Consequently, superpixels acquire better visual quality and their boundaries are more consistent with the object contours. Experimental results show that CONIC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms, in terms of both efficiency and segmentation effects.