19,268 research outputs found
Learning Contextual Variations for Video Segmentation
International audienceThis paper deals with video segmentation in vision systems. We focus on the maintenance of background models in long-term videos of changing environment which is still a real challenge in video surveillance. We propose an original weakly supervised method for learning contextual variations in videos. Our approach uses a clustering algorithm to automatically identify different contexts based on image content analysis. Then, state-of-the-art video segmentation algorithms (e.g. codebook, MoG) are trained on each cluster. The goal is to achieve a dynamic selection of background models. We have experimented our approach on a long video sequence (24 hours). The presented results show the segmentation improvement of our approach compared to codebook and MoG
Automatic Parameter Adaptation for Multi-object Tracking
Object tracking quality usually depends on video context (e.g. object
occlusion level, object density). In order to decrease this dependency, this
paper presents a learning approach to adapt the tracker parameters to the
context variations. In an offline phase, satisfactory tracking parameters are
learned for video context clusters. In the online control phase, once a context
change is detected, the tracking parameters are tuned using the learned values.
The experimental results show that the proposed approach outperforms the recent
trackers in state of the art. This paper brings two contributions: (1) a
classification method of video sequences to learn offline tracking parameters,
(2) a new method to tune online tracking parameters using tracking context.Comment: International Conference on Computer Vision Systems (ICVS) (2013
Context-aware Synthesis for Video Frame Interpolation
Video frame interpolation algorithms typically estimate optical flow or its
variations and then use it to guide the synthesis of an intermediate frame
between two consecutive original frames. To handle challenges like occlusion,
bidirectional flow between the two input frames is often estimated and used to
warp and blend the input frames. However, how to effectively blend the two
warped frames still remains a challenging problem. This paper presents a
context-aware synthesis approach that warps not only the input frames but also
their pixel-wise contextual information and uses them to interpolate a
high-quality intermediate frame. Specifically, we first use a pre-trained
neural network to extract per-pixel contextual information for input frames. We
then employ a state-of-the-art optical flow algorithm to estimate bidirectional
flow between them and pre-warp both input frames and their context maps.
Finally, unlike common approaches that blend the pre-warped frames, our method
feeds them and their context maps to a video frame synthesis neural network to
produce the interpolated frame in a context-aware fashion. Our neural network
is fully convolutional and is trained end to end. Our experiments show that our
method can handle challenging scenarios such as occlusion and large motion and
outperforms representative state-of-the-art approaches.Comment: CVPR 2018, http://graphics.cs.pdx.edu/project/ctxsy
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