Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking

We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking. The key innovation of the proposed method is to perform group feature selection across both channel and spatial dimensions, thus to pinpoint the structural relevance of multi-channel features to the filtering system. In contrast to the widely used spatial regularisation or feature selection methods, to the best of our knowledge, this is the first time that channel selection has been advocated for DCF-based tracking. We demonstrate that our GFS-DCF method is able to significantly improve the performance of a DCF tracker equipped with deep neural network features. In addition, our GFS-DCF enables joint feature selection and filter learning, achieving enhanced discrimination and interpretability of the learned filters. To further improve the performance, we adaptively integrate historical information by constraining filters to be smooth across temporal frames, using an efficient low-rank approximation. By design, specific temporal-spatial-channel configurations are dynamically learned in the tracking process, highlighting the relevant features, and alleviating the performance degrading impact of less discriminative representations and reducing information redundancy. The experimental results obtained on OTB2013, OTB2015, VOT2017, VOT2018 and TrackingNet demonstrate the merits of our GFS-DCF and its superiority over the state-of-the-art trackers. The code is publicly available at https://github.com/XU-TIANYANG/GFS-DCF

Locating the gamma-ray emission site in Fermi/LAT blazars from correlation analysis between 37 GHz radio and gamma-ray light curves

We address the highly debated issue of constraining the gamma-ray emission region in blazars from cross-correlation analysis using discrete correlation function between radio and gamma-ray light curves. The significance of the correlations is evaluated using two different approaches: simulating light curves and mixed source correlations. The cross-correlation analysis yielded 26 sources with significant correlations. In most of the sources, the gamma-ray peaks lead the radio with time lags in the range +20 and +690 days, whereas in sources 1633+382 and 3C 345 we find the radio emission to lead the gamma rays by -15 and -40 days, respectively. Apart from the individual source study, we stacked the correlations of all sources and also those based on sub-samples. The time lag from the stacked correlation is +80 days for the whole sample and the distance travelled by the emission region corresponds to 7 pc. We also compared the start times of activity in radio and gamma rays of the correlated flares using Bayesian block representation. This shows that most of the flares at both wavebands start at almost the same time, implying a co-spatial origin of the activity. The correlated sources show more flares and are brighter in both bands than the uncorrelated ones.Comment: 15 pages, 8 figures and 4 tables. Published in MNRAS. Online-only Figure 6 is available as ancillary file with this submissio