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Video synthetic aperture radar (ViSAR) has been found very useful for the surveillance of ground moving targets. The target energy can be utilized for ground moving target tracking, while the dynamic shadows of moving targets enable an alternative tracking approach. However, neither of these two approaches can stand alone to provide reliable target tracking. The smeared shadow and energy both degrade the tracking performance when the target is maneuvering.A moving target tracking framework based on the joint kernelized correlation filter (JKCF) has been developed. Based on the feature training of JKCF, the target is tracked by combining its shadow in the sequential SAR imagery and the corresponding energy in the range-Doppler (RD) spectra. Aiming at the problems of tracking drift and collapse, interactive processing is adopted to enhance the target positioning and feature update based on the confidence assessment. By cooperating with the initialization and feature update strategy, the tracking success rate and precision can be improved significantly.

During the COVID-19 epidemic, the news is overwhelming in people's daily life. So, we aim to extract key information from a large amount of public news. This paper focus on the daily sentiment distribution of news and public opinion on Weibo that refers to the key word COVID-19. First, we refining the key news from all the news in a day to deal with long and large news data. Second, we transformer the headline into a highdimensional vector. And then, divided them into k categories on the strength of k-means clustering algorithm. Finally, choose the closet news to the mean vector as the key news of the day. Moreover, we conduct sentiment analysis on all key news and Weibo data. By comparing the sentiment trend of news and Weibo, this study provides a new channel to analyze social public opinion. CCS CONCEPTS • General and reference → Cross-computing tools and techniques → Empirical studies