2,453 research outputs found
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
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Because of their recent introduction, self-driving cars and advanced driver
assistance system (ADAS) equipped vehicles have had little opportunity to
learn, the dangerous traffic (including near-miss incident) scenarios that
provide normal drivers with strong motivation to drive safely. Accordingly, as
a means of providing learning depth, this paper presents a novel traffic
database that contains information on a large number of traffic near-miss
incidents that were obtained by mounting driving recorders in more than 100
taxis over the course of a decade. The study makes the following two main
contributions: (i) In order to assist automated systems in detecting near-miss
incidents based on database instances, we created a large-scale traffic
near-miss incident database (NIDB) that consists of video clip of dangerous
events captured by monocular driving recorders. (ii) To illustrate the
applicability of NIDB traffic near-miss incidents, we provide two primary
database-related improvements: parameter fine-tuning using various near-miss
scenes from NIDB, and foreground/background separation into motion
representation. Then, using our new database in conjunction with a monocular
driving recorder, we developed a near-miss recognition method that provides
automated systems with a performance level that is comparable to a human-level
understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition,
61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201
CED: Color Event Camera Dataset
Event cameras are novel, bio-inspired visual sensors, whose pixels output
asynchronous and independent timestamped spikes at local intensity changes,
called 'events'. Event cameras offer advantages over conventional frame-based
cameras in terms of latency, high dynamic range (HDR) and temporal resolution.
Until recently, event cameras have been limited to outputting events in the
intensity channel, however, recent advances have resulted in the development of
color event cameras, such as the Color-DAVIS346. In this work, we present and
release the first Color Event Camera Dataset (CED), containing 50 minutes of
footage with both color frames and events. CED features a wide variety of
indoor and outdoor scenes, which we hope will help drive forward event-based
vision research. We also present an extension of the event camera simulator
ESIM that enables simulation of color events. Finally, we present an evaluation
of three state-of-the-art image reconstruction methods that can be used to
convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to
visualise the event stream, and for use in downstream vision applications.Comment: Conference on Computer Vision and Pattern Recognition Workshop
A comprehensive survey on recent deep learning-based methods applied to surgical data
Minimally invasive surgery is highly operator dependant with a lengthy
procedural time causing fatigue to surgeon and risks to patients such as injury
to organs, infection, bleeding, and complications of anesthesia. To mitigate
such risks, real-time systems are desired to be developed that can provide
intra-operative guidance to surgeons. For example, an automated system for tool
localization, tool (or tissue) tracking, and depth estimation can enable a
clear understanding of surgical scenes preventing miscalculations during
surgical procedures. In this work, we present a systematic review of recent
machine learning-based approaches including surgical tool localization,
segmentation, tracking, and 3D scene perception. Furthermore, we provide a
detailed overview of publicly available benchmark datasets widely used for
surgical navigation tasks. While recent deep learning architectures have shown
promising results, there are still several open research problems such as a
lack of annotated datasets, the presence of artifacts in surgical scenes, and
non-textured surfaces that hinder 3D reconstruction of the anatomical
structures. Based on our comprehensive review, we present a discussion on
current gaps and needed steps to improve the adaptation of technology in
surgery.Comment: This paper is to be submitted to International journal of computer
visio
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