25,936 research outputs found
Sparse optical flow regularisation for real-time visual tracking
Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes
Dynamic Face Video Segmentation via Reinforcement Learning
For real-time semantic video segmentation, most recent works utilised a
dynamic framework with a key scheduler to make online key/non-key decisions.
Some works used a fixed key scheduling policy, while others proposed adaptive
key scheduling methods based on heuristic strategies, both of which may lead to
suboptimal global performance. To overcome this limitation, we model the online
key decision process in dynamic video segmentation as a deep reinforcement
learning problem and learn an efficient and effective scheduling policy from
expert information about decision history and from the process of maximising
global return. Moreover, we study the application of dynamic video segmentation
on face videos, a field that has not been investigated before. By evaluating on
the 300VW dataset, we show that the performance of our reinforcement key
scheduler outperforms that of various baselines in terms of both effective key
selections and running speed. Further results on the Cityscapes dataset
demonstrate that our proposed method can also generalise to other scenarios. To
the best of our knowledge, this is the first work to use reinforcement learning
for online key-frame decision in dynamic video segmentation, and also the first
work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at:
https://github.com/mapleandfire/300VW-Mas
Unsupervised Learning of Edges
Data-driven approaches for edge detection have proven effective and achieve
top results on modern benchmarks. However, all current data-driven edge
detectors require manual supervision for training in the form of hand-labeled
region segments or object boundaries. Specifically, human annotators mark
semantically meaningful edges which are subsequently used for training. Is this
form of strong, high-level supervision actually necessary to learn to
accurately detect edges? In this work we present a simple yet effective
approach for training edge detectors without human supervision. To this end we
utilize motion, and more specifically, the only input to our method is noisy
semi-dense matches between frames. We begin with only a rudimentary knowledge
of edges (in the form of image gradients), and alternate between improving
motion estimation and edge detection in turn. Using a large corpus of video
data, we show that edge detectors trained using our unsupervised scheme
approach the performance of the same methods trained with full supervision
(within 3-5%). Finally, we show that when using a deep network for the edge
detector, our approach provides a novel pre-training scheme for object
detection.Comment: Camera ready version for CVPR 201
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