Good features to track

Shi, Jianbo; Tomasi,

doi:10.1109/cvpr.1994.323794

Cited by 2,841 publications

(540 citation statements)

References 11 publications

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“…If they are needed, the focus merge registration option is accomplished using multiresolution Kanade-Lucas-Tomasi (KLT) feature tracking based on the work of Lucas and Kanade (1981) and Shi and Tomasi (1994) with Harris corner detection (based on Harris and Stephens 1988) to identify feature points and track them between image pairs in the stack. The blending option combines the images using multi-resolution spline-based image blending (based on Burt and Adelson 1983).…”

Section: Onboard Focus Mergementioning

confidence: 99%

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

et al. 2012

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The Mars Science Laboratory (MSL) Mars Hand Lens Imager (MAHLI) investigation will use a 2-megapixel color camera with a focusable macro lens aboard the rover, Curiosity, to investigate the stratigraphy and grain-scale texture, structure, mineralogy, and morphology of geologic materials in northwestern Gale crater. Of particular interest is the stratigraphic record of a ∼5 km thick layered rock sequence exposed on the slopes of Aeolis Mons (also known as Mount Sharp). The instrument consists of three parts, a camera head mounted on the turret at the end of a robotic arm, an electronics and data storage assembly located inside the rover body, and a calibration target mounted on the robotic arm shoulder azimuth actuator housing. MAHLI can acquire in-focus images at working distances from ∼2.1 cm to infinity. At the minimum working distance, image pixel scale is ∼14 µm per pixel and very coarse silt grains can be resolved. At the working distance of the Mars Exploration Rover Microscopic Imager cameras aboard Spirit and Opportunity, MAHLI's resolution is comparable at ∼30 µm per pixel. Onboard capabilities include autofocus, autoexposure, sub-framing, video imaging, Bayer pattern color interpolation, lossy and lossless compression, focus merging of up to 8 focus stack images, white light and longwave ultraviolet (365 nm) illumination of nearby subjects, and 8 gigabytes of non-volatile memory data storage.

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Section: Onboard Focus Mergementioning

confidence: 99%

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

et al. 2012

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“…Los puntos salientes se obtienen cuando los dos valores propios de la matriz hessiana, dada por (3), en el punto "esquina" superan un umbral; caso contrario, se sabe que solo un valor propio alto es un borde; si los dos son bajos es una región lisa Shi and Tomasi (1994).…”

Section: Puntos Salientesunclassified

Generación de Regiones con Potencial de Contener Peatones usando Reconstrucción 3D No Densa a partir de Visión Monocular

Zubiaguirre-Bergen

Torres-Torriti

Flores-Calero

2018

Rev. iberoam. autom. inform. ind.

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ResumenLos accidentes de tráfico son un problema de salud pública a escala mundial, por el alto número de víctimas humanas y los elevados costos económicos y sociales que generan. En este contexto, los peatones se encuentran entre los elementos más importantes y vulnerables de la escena vial que necesitan ser protegidos. Es así que en este trabajo se presenta una innovadora propuesta utilizado la información visual monocular para emular la visión estéreo, y a partir de ello: i) generar regiones de interés (ROIs) con alta posibilidad de contener un peatón, y ii) estimar la trayectoria del vehículo. Los experimentos han sido desarrollados sobre una base de datos de imágenes tomadas en varias calles de la ciudad de Santiago (Región-Metropolitana), Chile. Esta información fue obtenida usando una plataforma experimental en condiciones reales de conducción durante el día. La tasa de detección de ROIs es del 86,6 % para distancias menores a 20 metros, 82,9 % para distancias menores a 30 metros y del 76,2 % para distancias menores a 40 metros.Palabras Clave: Peatones, accidentes, tráfico, visión monocular, visión estéreo, trayectoria, ROIs.Generation of regions of interest with high potential to contain pedestrians using non-dense 3D reconstruction from monocular vision. AbstractTraffic accidents are a global public health problem, due to the high number of human victims and the elevated economic and social costs that generate. In this context, pedestrians are among the most important and vulnerable elements of the road scene that need to be protected. It is thus that, in this work an innovative proposal is presented where the monocular visual information is used to simulate the stereo vision, and from this: i) generate regions of interest (ROIs) with high possibility of containing a pedestrian, and ii) estimate the trajectory of the vehicle. Experiments have been developed into a dataset of images taken in several streets of Santiago (Región Metropolitana), Chile. This database was obtained using an experimental vehicle under real driving conditions during the day. The ROI detection rate is 86,6 % for distances less than 20 meters, 82,9 % for distances less than 30 meters and 76,2 % for distances less than 40 meters. Keywords:Pedestrian, accidents, traffic, monocular vision, stereo vision, trajectory, ROIs. IntroducciónLos accidentes de tráfico causan al año 1,3 millones de muertes y entre 20 y 50 millones de heridos en el mundo, siendo así la primera causa de muerte de jóvenes entre 15 y 29 años de edad. El 91 % de ellos ocurren en países de bajo y mediano ingresos. Por otra parte, niños, peatones, ciclistas y personas de * Autor para correspondencia: mjflores@espe.edu.ec Este artículo ha sido aceptado para su publicación en un futuro número de RIAI. Su contenido es definitivo y únicamente cambiará en la versión final la información relativa al volumen, número y número de páginas.

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“…(24) The Shi-Tomasi operator (Eq. 25) [35] responds strongly to corners and filters out most edges (since one of the eigenvalues is nearly zero along edges). This is not suitable for a geodesic active contour edge stopping function.…”

Section: Comparison Of Color Feature Detectorsmentioning

confidence: 99%

“…Several common color (edge/corner) feature indicator functions were evaluated for comparison purposes, including Harris [34], Shi-Tomasi [35], Cumani [36] feature detectors and determinant of the Beltrami metric tensor [39].…”

Section: Comparison Of Color Feature Detectorsmentioning

confidence: 99%

Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking

Bunyak

Palaniappan²,

Nath³

et al. 2007

JMM

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This paper makes new contributions in motion detection, object segmentation and trajectory estimation to create a successful object tracking system. A new efficient motion detection algorithm referred to as the flux tensor is used to detect moving objects in infrared video without requiring background modeling or contour extraction. The flux tensor-based motion detector when applied to infrared video is more accurate than thresholding "hot-spots", and is insensitive to shadows as well as illumination changes in the visible channel. In real world monitoring tasks fusing scene information from multiple sensors and sources is a useful core mechanism to deal with complex scenes, lighting conditions and environmental variables. The object segmentation algorithm uses level set-based geodesic active contour evolution that incorporates the fusion of visible color and infrared edge informations in a novel manner. Touching or overlapping objects are further refined during the segmentation process using an appropriate shape-based model. Multiple object tracking using correspondence graphs is extended to handle groups of objects and occlusion events by Kalman filter-based cluster trajectory analysis and watershed segmentation. The proposed object tracking algorithm was successfully tested on several difficult outdoor multispectral videos from stationary sensors and is not confounded by shadows or illumination variations.

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Good features to track

Cited by 2,841 publications

References 11 publications

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

Curiosity’s Mars Hand Lens Imager (MAHLI) Investigation

Generación de Regiones con Potencial de Contener Peatones usando Reconstrucción 3D No Densa a partir de Visión Monocular

Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking

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