Object detection and tracking with active camera on motion vectors of feature points and particle filter

Chen, Yong; Shang, Lei; Hu, Eric

doi:10.1063/1.4809768

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…But these approaches do not take into account the challenges of moving camera. J. S. Lim and W. H. Kim [17], Y. Chen et al [18] tried to calculate translation vector between two consecutive frames (or two frames from stereo camera).…”

Section: Introductionmentioning

confidence: 99%

A New Framework of Moving Object Tracking based on Object Detection-Tracking with Removal of Moving Features

Ngoc¹,

Thành²,

Bao³

2020

IJACSA

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Object Tracking (OT) on a Moving Camera socalled Moving Object Tracking (MOT) is extremely vital in Computer Vision. While other conventional tracking methods based on fixed camera can only track the objects in its range, a moving camera can tackle this issue by following the objects. Moreover, single tracker is used widely to track object but it is not effective due to the moving camera because the challenges such as sudden movements, blurring, pose variation. The paper proposes a method inherited by tracking by detection approach. It integrates a single tracker with object detection method. The proposed tracking system can track object efficiency and effectively because object detection method can be used to find the tracked object again if the single tracker loses track. Three main contributions are presented in the paper as follow. First, the proposed Unified Visual based-MOT system can do the tasks such as Localization, 3D Environment Reconstruction and Tracking based on Stereo Camera and Inertial Measurement Unit (IMU). Second, it takes into account camera motion and the moving objects to improve the precision rate in localization and tracking. Third, proposed tracking system based on integration of single tracker as Deep Particle Filter and Object Detection as Yolov3. The overall system is tested on the dataset KITTI 2012, and it has achieved a good accuracy rate in real time.

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Section: Introductionmentioning

confidence: 99%

A New Framework of Moving Object Tracking based on Object Detection-Tracking with Removal of Moving Features

Ngoc¹,

Thành²,

Bao³

2020

IJACSA

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“…In this work, focus is on motion features (trajectories). Motion trajectories are informative, compact, and spatiotemporally continuous, which makes them useful for action recognition [13,14]. A novel approach that does not follow the standard steps and accordingly avoids the aforementioned difficulties is presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection in Car Traffic With Implementation of Optical Sensors

2020

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In this investigation, the problem of moving object detection -without any knowledge -is classified. It describes a technique that will allow real-time localization with usage of IR sensors. The proposed algorithm is simplistic, and in the future, it might be implemented into any vehicle, premium or entry level. It is guided by AI that must calculate its next moves in the blink of an eye without user noticing any delays. The main problem of moving object recognition was extraction of proper features, description of the events, and choice of only the crucial ones. The presented novel approach does not follow any standard algorithms. It is a practical hardware implementation of custom solution, based on processing system, which can be well situated in the safety modules of future cars.

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“…Este enfoque fue probado en entornos de tráfico real obteniendo excelentes resultados. Un nuevo método para la detección de objetos en movimientos con fondos no estacionarios fue realizado por [10]. En los cambios de rotación o de escala producidos por la cámara, el algoritmo de SURF es usado para la extracción de las características invariantes.…”

Section: Introductionunclassified

Detección de movimiento en tiempo real utilizando flujo óptico

Pérez¹,

Martínez²,

Vera³

et al. 2015

Vis. Electron.

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Se muestra la implementación de algoritmos de flujo óptico y extracción de características para el reconocimiento y detección de objetos en tiempo real de una secuencia de imágenes. Se implementaron algoritmos de flujo óptico y de detección de características SIFT, SURF y un estudio sobre la influencia de los cambios de iluminación en el ambiente de la escena analizando el funcionamiento y desempeño. Durante los cambios de iluminación se encontró que los puntos de interés entre dos imágenes consecutivas se reducen debido a que los algoritmos de detección no encuentran patrones similares y todos los niveles de intensidad son diferentes. Los resultados experimentales muestran que el algoritmo propuesto es funcional en escenas donde la luminosidad es constante y no es inferior a 1 Lux, se entrega una herramienta útil para sistemas de vigilancia y robótica móvil.

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Object detection and tracking with active camera on motion vectors of feature points and particle filter

Cited by 5 publications

References 10 publications

A New Framework of Moving Object Tracking based on Object Detection-Tracking with Removal of Moving Features

A New Framework of Moving Object Tracking based on Object Detection-Tracking with Removal of Moving Features

Moving Object Detection in Car Traffic With Implementation of Optical Sensors

Detección de movimiento en tiempo real utilizando flujo óptico

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