4,822 research outputs found
How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation
Real-time semantic segmentation on high-resolution videos is challenging due
to the strict requirements of speed. Recent approaches have utilized the
inter-frame continuity to reduce redundant computation by warping the feature
maps across adjacent frames, greatly speeding up the inference phase. However,
their accuracy drops significantly owing to the imprecise motion estimation and
error accumulation. In this paper, we propose to introduce a simple and
effective correction stage right after the warping stage to form a framework
named Tamed Warping Network (TWNet), aiming to improve the accuracy and
robustness of warping-based models. The experimental results on the Cityscapes
dataset show that with the correction, the accuracy (mIoU) significantly
increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8
FPS. For non-rigid categories such as "human" and "object", the improvements of
IoU are even higher than 18 percentage points
A group sparsity-driven approach to 3-D action recognition
In this paper, a novel 3-D action recognition method based on sparse representation is presented. Silhouette images from multiple cameras are combined to obtain motion history volumes (MHVs). Cylindrical Fourier transform of MHVs is used as action descriptors. We assume that a test sample has a sparse representation in the space of training samples. We cast the action classification problem as an optimization problem and classify actions using group sparsity based on l1 regularization. We show experimental results using the IXMAS multi-view database and demonstratethe superiority of our method, especially when observations are low resolution, occluded, and noisy and when
the feature dimension is reduced
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
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