Real-Time Visual Tracking
10 papers with code • 0 benchmarks • 1 datasets
These leaderboards are used to track progress in Real-Time Visual Tracking
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
Finally, we use a pre-rendered sparse viewpoint model to create a joint posterior probability for the object pose.
This paper considers a scenario of pursuing a moving target that may switch behaviors due to external factors in a dynamic environment by motion estimation using visual sensors.
The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image.
In this paper, we introduce a novel real-time visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods.
Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical.
By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity.
Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching.