22,275 research outputs found
Recurrent 3D Pose Sequence Machines
3D human articulated pose recovery from monocular image sequences is very
challenging due to the diverse appearances, viewpoints, occlusions, and also
the human 3D pose is inherently ambiguous from the monocular imagery. It is
thus critical to exploit rich spatial and temporal long-range dependencies
among body joints for accurate 3D pose sequence prediction. Existing approaches
usually manually design some elaborate prior terms and human body kinematic
constraints for capturing structures, which are often insufficient to exploit
all intrinsic structures and not scalable for all scenarios. In contrast, this
paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically
learn the image-dependent structural constraint and sequence-dependent temporal
context by using a multi-stage sequential refinement. At each stage, our RPSM
is composed of three modules to predict the 3D pose sequences based on the
previously learned 2D pose representations and 3D poses: (i) a 2D pose module
extracting the image-dependent pose representations, (ii) a 3D pose recurrent
module regressing 3D poses and (iii) a feature adaption module serving as a
bridge between module (i) and (ii) to enable the representation transformation
from 2D to 3D domain. These three modules are then assembled into a sequential
prediction framework to refine the predicted poses with multiple recurrent
stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset
show that our RPSM outperforms all state-of-the-art approaches for 3D pose
estimation.Comment: Published in CVPR 201
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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