Moving Object and Shadow Detection Algorithm Using Lab Color Space

A color indicator detection algorithm under different illumination conditions is proposed. First, based on the similarity between consecutive video frames in channel L of Lab color space, background image can be determined. Differentiation of a frame and the background can identify the motion region, and thus the search area for the color indicator is greatly reduced. Second, the convex hull of motion region is specified and sampling is taken within it. By assigning the weight, seeds can be determined using clustering method. Finally, region growing is implemented by applying Bayesian decision with minimal error ratio. The method is applicable to more different conditions and produces better results compared with traditional color-threshold vector method.

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Shadow Detection and Removal from a Single Image Using LAB Color Space

Cybernetics and Information Technologies ◽

10.2478/cait-2013-0009 ◽

2013 ◽

Vol 13 (1) ◽

pp. 95-103 ◽

Cited By ~ 20

Author(s):

Saritha Murali ◽

V. K. Govindan

Keyword(s):

Computer Vision ◽

Color Space ◽

Mean Value ◽

Shadow Detection ◽

Simple Method ◽

Shadow Boundary ◽

Lab Color Space ◽

The Mean ◽

Computer Vision Applications ◽

Rgb Image

Abstract A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a pre-processing task in many computer vision applications. This paper proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image. The shadow removal is done by multiplying the shadow region by a constant. Shadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.703.304 ◽

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.

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Study of Video Object Detection and Shadow Suppression Algorithms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.596.374 ◽

2014 ◽

Vol 596 ◽

pp. 374-378

Author(s):

Qi Lin Gai ◽

Guo Qiang Wang

Keyword(s):

Object Detection ◽

Video Surveillance ◽

Color Space ◽

Moving Object Detection ◽

Moving Object ◽

Shadow Detection ◽

Behavioral Characteristics ◽

Video Object ◽

Intelligent Video Surveillance ◽

Shadow Suppression

In the field of intelligent video surveillance and the multimedia applications we usually need to detect the moving object which is separated from the background. The results of the moving object detection would affect the subsequent identification, classification and tracking. Meanwhile shadow detection and suppression are also the important technology of the intelligent video surveillance. Because the moving object and shadow usually has the same behavioral characteristics, which has led to the errors of object recognition and tracking and affect the robustness of system seriously. This article studies the principle and algorithm of background subtraction, and has a detailed discussion and analysis. Shadow detection and suppression algorithms based on the YUV color space for processing. The experiment result shows that the algorithms for moving object detection with a better accuracy and stability of this paper.

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

Remote Sensing ◽

10.3390/rs13040699 ◽

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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Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽

10.3390/electronics10141665 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1665

Author(s):

Jakub Suder ◽

Kacper Podbucki ◽

Tomasz Marciniak ◽

Adam Dąbrowski

Keyword(s):

Edge Detection ◽

Optimal Path ◽

Color Space ◽

Low Complexity ◽

Detection Algorithm ◽

Lane Detection ◽

Detection Methods ◽

Raspberry Pi ◽

Line Detection ◽

Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

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