A HSV Space Shadow Method Based Human Motion Detection

2013 ◽  
Vol 706-708 ◽  
pp. 597-600
Author(s):  
Hui Dang
Keyword(s):  
Color Space ◽  
Human Action ◽  
Human Motion ◽  
Shadow Detection ◽  
Fundamental Issue ◽  
Color Information ◽  
Shadow Method ◽  
Hsv Color Space ◽  
Human Motion Detection ◽  
Action Analysis

Human Action Analysis is a fundamental issue that can be applied to different application domains. In this paper, we present a HSV color space based shadow method. The process of the algorithm mainly includes three steps: motional object detection, shadow detection of the object and post-processing. In order to enhance the accuracy of shadow detection, the value of and in the method can be select elaborately. The experiment result indicates the presented algorithm can detect shadow effectively and make full use of the color information.

Study on Detection of Human Motion Using a RGB Color Space Shadow Method with Mechanics Properties

2013 ◽  
Vol 703 ◽  
pp. 304-307
Author(s):  
Bao Dong Yan ◽  
Ying Yu
Keyword(s):  
Object Detection ◽  
Detection Threshold ◽  
Color Space ◽  
Detection Algorithm ◽  
Human Motion ◽  
Image Sequences ◽  
Shadow Detection ◽  
Motion Feature ◽  
Human Motion Detection ◽  
Rgb Color Space

The aim of human mechanics is to reveal the mechanics properties of human motion. Especially, the purpose of human motion detection is detecting the moving people from continuous image sequences, extracting human body segments and then getting motion feature. The paper presents a shadow detection algorithm based on covariance difference operator based RGB color space and discusses its mechanics properties. The presented algorithm includes four steps: object detection, suspected shadow detection, shadow detection and post processing. The presented algorithm of adaptive shadow detection threshold is adopted to suppress the effect of shadow in moving object detection more effectively. The experiment results show the algorithm presented in this paper can detect shadow effectively.

Designing FIRA Medium-Sized Soccer Robot Vision System Using Particle Swarm Optimization

2015 ◽  
Vol 764-765 ◽  
pp. 675-679
Author(s):  
Ching Yi Chen ◽  
Chi Chiang Ko
Keyword(s):  
Particle Swarm Optimization ◽  
Vision System ◽  
Color Space ◽  
Particle Swarm ◽  
Robot Vision ◽  
Color Information ◽  
Swarm Optimization ◽  
Color Feature ◽  
Hsv Color Space ◽  
Lighting Conditions

Enabling FIRA medium-sized soccer robots to recognize target objects according to color information requires that competing teams manually set the range of colors according to ambient lighting conditions prior to games. This color information is used to differentiate features of target objects, such as the ball, the goals, and the field. Constructing a color-feature model such as this is extremely time-consuming and the resulting model is unable to adapt dynamically to changes in lighting conditions. This study applied a look-up table method to execute RGB-HSV color space conversion to accelerate video processing. A particle swarm optimization (PSO) scheme was developed to detect the color-feature parameters of the target objects in the HSV color space. This enables the automatic completion of color-feature modeling and the construction of the knowledge model required by the robot for object recognition. Experiment results demonstrate that the proposed method is capable of enhancing the robustness of the robot vision system in determining changes in lighting conditions. In addition, the manpower and time required to set robot parameters prior to games were reduced significantly.

HSV color space based pupil position locating method

2014 ◽  
Author(s):  
Peng Cao ◽  
Qijie Zhao ◽  
Dawei Tu ◽  
Hui Shao
Keyword(s):  
Color Space ◽  
Hsv Color Space

FOREGROUND IMAGE EXTRCTION IN THE HSV COLOR SPACE

2010 ◽  
Vol 7 (7) ◽  
pp. 1-4
Author(s):  
Jyh-Yeong Chang ◽  
Jia-Jye Shyu ◽  
Yi-Cheng Luo
Keyword(s):  
Color Space ◽  
Hsv Color Space

scholarly journals Anisotropic, Wrinkled, and Crack-Bridging Structure for Ultrasensitive, Highly Selective Multidirectional Strain Sensors

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Heng Zhang ◽  
Dan Liu ◽  
Jeng-Hun Lee ◽  
Haomin Chen ◽  
Eunyoung Kim ◽  
...  
Keyword(s):  
Rational Design ◽  
Full Range ◽  
Human Motion ◽  
Strain Sensors ◽  
Gauge Factor ◽  
Wearable Electronics ◽  
Trade Off ◽  
Human Motion Detection ◽  
Sensing Applications ◽  
Anisotropic Structures

AbstractFlexible multidirectional strain sensors are crucial to accurately determining the complex strain states involved in emerging sensing applications. Although considerable efforts have been made to construct anisotropic structures for improved selective sensing capabilities, existing anisotropic sensors suffer from a trade-off between high sensitivity and high stretchability with acceptable linearity. Here, an ultrasensitive, highly selective multidirectional sensor is developed by rational design of functionally different anisotropic layers. The bilayer sensor consists of an aligned carbon nanotube (CNT) array assembled on top of a periodically wrinkled and cracked CNT–graphene oxide film. The transversely aligned CNT layer bridge the underlying longitudinal microcracks to effectively discourage their propagation even when highly stretched, leading to superior sensitivity with a gauge factor of 287.6 across a broad linear working range of up to 100% strain. The wrinkles generated through a pre-straining/releasing routine in the direction transverse to CNT alignment is responsible for exceptional selectivity of 6.3, to the benefit of accurate detection of loading directions by the multidirectional sensor. This work proposes a unique approach to leveraging the inherent merits of two cross-influential anisotropic structures to resolve the trade-off among sensitivity, selectivity, and stretchability, demonstrating promising applications in full-range, multi-axis human motion detection for wearable electronics and smart robotics.

scholarly journals Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

2021 ◽  
Vol 13 (4) ◽  
pp. 699
Author(s):  
Tingting Zhou ◽  
Haoyang Fu ◽  
Chenglin Sun ◽  
Shenghan Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Mean Shift ◽  
Color Space ◽  
Compensation Method ◽  
Shadow Detection ◽  
Processing Unit ◽  
Shadow Region ◽  
Remote Sensing Images ◽  
Shadow Compensation

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

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