7,833 research outputs found
Micro Fourier Transform Profilometry (FTP): 3D shape measurement at 10,000 frames per second
Recent advances in imaging sensors and digital light projection technology
have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces
of complex-shaped objects to be captured with improved resolution and accuracy.
However, due to the large number of projection patterns required for phase
recovery and disambiguation, the maximum fame rates of current 3D shape
measurement techniques are still limited to the range of hundreds of frames per
second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro
Fourier Transform Profilometry (FTP), which can capture 3D surfaces of
transient events at up to 10,000 fps based on our newly developed high-speed
fringe projection system. Compared with existing techniques, FTP has the
prominent advantage of recovering an accurate, unambiguous, and dense 3D point
cloud with only two projected patterns. Furthermore, the phase information is
encoded within a single high-frequency fringe image, thereby allowing
motion-artifact-free reconstruction of transient events with temporal
resolution of 50 microseconds. To show FTP's broad utility, we use it to
reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating
fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a
flying dart, which were previously difficult or even unable to be captured with
conventional approaches.Comment: This manuscript was originally submitted on 30th January 1
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 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
Object Tracking from Unstabilized Platforms by Particle Filtering with Embedded Camera Ego Motion
Visual tracking with moving cameras is a challenging task. The global motion induced by the moving camera moves the target object outside the expected search area, according to the object dynamics. The typical approach is to use a registration algorithm to compensate the camera motion. However, in situations involving several moving objects, and backgrounds highly affected by the aperture problem, image registration quality may be very low, decreasing dramatically the performance of the tracking. In this work, a novel approach is proposed to successfully tackle the tracking with moving cameras in complex situations, which involve several independent moving objects. The key idea is to compute several hypotheses for the camera motion, instead of estimating deterministically only one. These hypotheses are combined with the object dynamics in a Particle Filter framework to predict the most probable object locations. Then, each hypothetical object location is evaluated by the measurement model using a spatiogram, which is a region descriptor based on color and spatial distributions. Experimental results show that the proposed strategy allows to accurately track an object in complex situations affected by strong ego motion
Recommended from our members
Tracking a table tennis ball for umpiring purposes using a multi-agent system
Tracking a table tennis ball for umpiring purposes is a challenging task as, in real-match scenarios, the ball travels fast and can become occluded or merged with other background objects. This paper presents the design of a multi-view based tracking system that can overcome the challenges of tracking a ball in real match sequences. The system has been tested on a complete table tennis rally and the results are very promising. The system is able to continuously track the ball with only marginal variations in detection. Furthermore, the initialization of the multi-camera system means it is both a portable and cost-effective solution for umpiring purposes
- …