Priya Mariam Raju

TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: Experimental results show that the proposed method has better detection accuracy, in terms of precision, recall and F1 Score, when compared with the state-of-the-art methods of copy-move forgery detection, under conditions of plain copy- move forgery as well as post-processing conditions like brightness changes, contrast adjustments, color reduction and blurring.

TL;DR: Extensive experimental analysis on benchmark datasets indicate that the proposed tracker is well suited for robust long-term tracking and is superior to other state of the art methods both qualitatively and quantitatively.

TL;DR: The proposed method introduces instance segmentation as an attention mechanism in object tracking framework, motivated by the strong localization property of segmented object masks, to incorporate target specific knowledge and strong discrimination ability.

TL;DR: A robust framework is proposed that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation and supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map.