876 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
Indoor assistance for visually impaired people using a RGB-D camera
In this paper a navigational aid for visually impaired people is presented. The system uses a RGB-D camera to perceive the environment and implements self-localization, obstacle detection and obstacle classification. The novelty of this work is threefold. First, self-localization is performed by means of a novel camera tracking approach that uses both depth and color information. Second, to provide the user with semantic information, obstacles are classified as walls, doors, steps and a residual class that covers isolated objects and bumpy parts on the floor. Third, in order to guarantee real time performance, the system is accelerated by offloading parallel operations to the GPU. Experiments demonstrate that the whole system is running at 9 Hz
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
A hybrid approach to simultaneous localization and mapping in indoors environment
This thesis will present SLAM in the current literature to benefit from then it will present the investigation results for a hybrid approach used where different algorithms using laser, sonar, and camera sensors were tested and compared. The contribution of this thesis is the development of a hybrid approach for SLAM that uses different sensors and where different factors are taken into consideration such as dynamic objects, and the development of a scalable grid map model with new sensors models for real time update of the map.The thesis will show the success found, difficulties faced and limitations of the algorithms developed which were simulated and experimentally tested in an indoors environment
Survey on video anomaly detection in dynamic scenes with moving cameras
The increasing popularity of compact and inexpensive cameras, e.g.~dash
cameras, body cameras, and cameras equipped on robots, has sparked a growing
interest in detecting anomalies within dynamic scenes recorded by moving
cameras. However, existing reviews primarily concentrate on Video Anomaly
Detection (VAD) methods assuming static cameras. The VAD literature with moving
cameras remains fragmented, lacking comprehensive reviews to date. To address
this gap, we endeavor to present the first comprehensive survey on Moving
Camera Video Anomaly Detection (MC-VAD). We delve into the research papers
related to MC-VAD, critically assessing their limitations and highlighting
associated challenges. Our exploration encompasses three application domains:
security, urban transportation, and marine environments, which in turn cover
six specific tasks. We compile an extensive list of 25 publicly-available
datasets spanning four distinct environments: underwater, water surface,
ground, and aerial. We summarize the types of anomalies these datasets
correspond to or contain, and present five main categories of approaches for
detecting such anomalies. Lastly, we identify future research directions and
discuss novel contributions that could advance the field of MC-VAD. With this
survey, we aim to offer a valuable reference for researchers and practitioners
striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie
Monocular detection and estimation of moving obstacles for robot navigation
The detection of motion and moving objects or persons with stationary monocular cameras has been extensively studied. However, those techniques fail if the camera is moving itself. In this paper, we present a method for detecting and estimating the position of moving objects using a monocular camera that is mounted in front of a mobile robot platform. The position estimates are used for obstacle avoidance and robot navigation. We apply image warping to compensate the egomotion of the camera. This allows us to use standard techniques for motion detection. The final position and velocity estimates are obtained using Extended Kalman Filters. Combined with a monocular scene reconstruction our approach allows the robust detection and avoidance of both static and moving obstacles by using a single monocular camera as the only sensor
BatMobility: Towards Flying Without Seeing for Autonomous Drones
Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and
lidar for autonomous operation. However, such optical sensors are error-prone
in bad lighting, inclement weather conditions including fog and smoke, and
around textureless or transparent surfaces. In this paper, we ask: is it
possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly
without seeing? We present BatMobility, a lightweight mmWave radar-only
perception system for UAVs that eliminates the need for optical sensors.
BatMobility enables two core functionalities for UAVs -- radio flow estimation
(a novel FMCW radar-based alternative for optical flow based on
surface-parallel doppler shift) and radar-based collision avoidance. We build
BatMobility using commodity sensors and deploy it as a real-time system on a
small off-the-shelf quadcopter running an unmodified flight controller. Our
evaluation shows that BatMobility achieves comparable or better performance
than commercial-grade optical sensors across a wide range of scenarios
04251 -- Imaging Beyond the Pinhole Camera
From 13.06.04 to 18.06.04, the
Dagstuhl Seminar 04251 ``Imaging Beyond the Pin-hole Camera. 12th Seminar on Theoretical Foundations of Computer Vision\u27\u27 was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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