6,370 research outputs found
Markerless Motion Capture via Convolutional Neural Network
A human motion capture system can be defined as a process that digitally records the movements of a person and then translates them into computer-animated images.
To achieve this goal, motion capture systems usually exploit different types of algorithms, which include techniques such as pose estimation or background subtraction: this latter aims at segmenting moving objects from the background under multiple challenging scenarios. Recently, encoder-decoder-type deep neural networks designed to accomplish this task have reached impressive results, outperforming classical approaches.
The aim of this thesis is to evaluate and discuss the predictions provided by the multi-scale convolutional neural network FgSegNet_v2, a deep learning-based method which represents the current state-of-the-art for implementing scene-specific background subtraction.
In this work, FgSegNet_v2 is trained and tested on BBSoF S.r.l. dataset, extending its scene- specific use to a more general application in several environments
BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip
A fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision
and video processing often applied as a pre-processing step
for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have
been proposed, however nearly all of the top-performing
ones are supervised. Crucially, their success relies upon
the availability of some annotated frames of the test video
during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature.
In this work, we propose a new, supervised, backgroundsubtraction algorithm for unseen videos (BSUV-Net) based
on a fully-convolutional neural network. The input to our
network consists of the current frame and two background
frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance
of overfitting, we also introduce a new data-augmentation
technique which mitigates the impact of illumination difference between the background frames and the current frame.
On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of
several metrics including F-measure, recall and precision.Accepted manuscrip
ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information
Object detection in wide area motion imagery (WAMI) has drawn the attention
of the computer vision research community for a number of years. WAMI proposes
a number of unique challenges including extremely small object sizes, both
sparse and densely-packed objects, and extremely large search spaces (large
video frames). Nearly all state-of-the-art methods in WAMI object detection
report that appearance-based classifiers fail in this challenging data and
instead rely almost entirely on motion information in the form of background
subtraction or frame-differencing. In this work, we experimentally verify the
failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a
heatmap-based fully convolutional neural network (CNN), and propose a novel
two-stage spatio-temporal CNN which effectively and efficiently combines both
appearance and motion information to significantly surpass the state-of-the-art
in WAMI object detection. To reduce the large search space, the first stage
(ClusterNet) takes in a set of extremely large video frames, combines the
motion and appearance information within the convolutional architecture, and
proposes regions of objects of interest (ROOBI). These ROOBI can contain from
one to clusters of several hundred objects due to the large video frame size
and varying object density in WAMI. The second stage (FoveaNet) then estimates
the centroid location of all objects in that given ROOBI simultaneously via
heatmap estimation. The proposed method exceeds state-of-the-art results on the
WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped
objects, as well as being the first proposed method in wide area motion imagery
to detect completely stationary objects.Comment: Main paper is 8 pages. Supplemental section contains a walk-through
of our method (using a qualitative example) and qualitative results for WPAFB
2009 datase
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