Cast Shadow Detection and Removal of Moving Objects from Video Based on HSV Color Space

2015 ◽  
Author(s):  
Kaushik Deb
Keyword(s):  
Moving Objects ◽  
Color Space ◽  
Shadow Detection ◽  
Hsv Color Space ◽  
Cast Shadow

scholarly journals 3D Reconstruction with Single-Shot Structured Light RGB Line Pattern

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4819
Author(s):  
Yikang Li ◽  
Zhenzhou Wang
Keyword(s):  
3D Reconstruction ◽  
Moving Objects ◽  
Structured Light ◽  
Color Space ◽  
Reconstruction Technique ◽  
Single Shot ◽  
Line Pattern ◽  
Hsv Color Space ◽  
Phase Map ◽  
Line Segmentation

Single-shot 3D reconstruction technique is very important for measuring moving and deforming objects. After many decades of study, a great number of interesting single-shot techniques have been proposed, yet the problem remains open. In this paper, a new approach is proposed to reconstruct deforming and moving objects with the structured light RGB line pattern. The structured light RGB line pattern is coded using parallel red, green, and blue lines with equal intervals to facilitate line segmentation and line indexing. A slope difference distribution (SDD)-based image segmentation method is proposed to segment the lines robustly in the HSV color space. A method of exclusion is proposed to index the red lines, the green lines, and the blue lines respectively and robustly. The indexed lines in different colors are fused to obtain a phase map for 3D depth calculation. The quantitative accuracies of measuring a calibration grid and a ball achieved by the proposed approach are 0.46 and 0.24 mm, respectively, which are significantly lower than those achieved by the compared state-of-the-art single-shot techniques.

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.

The Research of Motion Detection and Shadow Elimination in Video Surveillance

2012 ◽  
Vol 468-471 ◽  
pp. 2691-2694
Author(s):  
Zhi Li Qing ◽  
Yue Lin Chen
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Moving Objects ◽  
Color Space ◽  
Video Image ◽  
Experimental Results ◽  
Background Model ◽  
Hsv Color Space ◽  
Shadow Elimination

This paper studies the moving objects detect and shadow eliminate in video surveillance. Completed the background generated on the video image by study the mixed Gaussian background model, by transforming the image to hsv color space for processing, which achieve the elimination of shadows. The experimental results show the approach this paper use is effectively on the background generated and shadow remove.

scholarly journals An Efficient Real Time Object Tracking Based on HSV Value

2021 ◽  
Vol 9 (11) ◽  
pp. 1197-1203
Author(s):  
Sheikh Summerah
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Moving Objects ◽  
Color Space ◽  
Video Tracking ◽  
Frame Rate ◽  
Long Distance ◽  
Hsv Color Space ◽  
Video Frames ◽  
Testing Stage

Abstract: This study presents a strategy to automate the process to recognize and track objects using color and motion. Video Tracking is the approach to detect a moving item using a camera across the long distance. The basic goal of video tracking is in successive video frames to link target objects. When objects move quicker in proportion to frame rate, the connection might be particularly difficult. This work develops a method to follow moving objects in real-time utilizing HSV color space values and OpenCV in distinct video frames.. We start by deriving the HSV value of an object to be tracked and then in the testing stage, track the object. It was seen that the objects were tracked with 90% accuracy. Keywords: HSV, OpenCV, Object tracking,

Cast Shadow Removal in a Real-Time Environment

2013 ◽  
Vol 275-277 ◽  
pp. 2548-2554
Author(s):  
Hong Ying Zhang ◽  
Hong Li ◽  
Yi Gang Sun
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Space ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Shadow Removal ◽  
Hsv Color Space ◽  
Cast Shadow ◽  
Novel Algorithm

The cast shadows on the background of the object will distinctly affect the recognition of the foreground objects. Due to the limitation of shadow removal methods utilizing texture, a novel algorithm based on Gaussian Mixture Model (GMM) and HSV color space is proposed. Firstly, moving regions are detected using GMM. Secondly, we make two pre-classifiers accurate and adaptive to the change of shadow by using the features of shadow in RGB and HSV color space. Experimental results show that the proposed method is efficient and robust.

scholarly journals An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection

2019 ◽  
Vol 9 (23) ◽  
pp. 5042 ◽  
Author(s):  
Yugen Yi ◽  
Jiangyan Dai ◽  
Chengduan Wang ◽  
Jinkui Hou ◽  
Huihui Zhang ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Extreme Learning Machine ◽  
Moving Objects ◽  
Shadow Detection ◽  
Classification Model ◽  
Cast Shadow ◽  
Different Color ◽  
Learning Machine ◽  
Tracking Object ◽  
High Level

Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast-shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features of different channels in different color spaces and region-level features derived from the spatial correlation of neighboring pixels are extracted from the foreground. Second, an ELM-based classification model is developed by labelled shadow and un-shadow points, which is able to rapidly distinguish the points in the new input whether they belong to shadows or not. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on two publicly common datasets including 13 different scenes demonstrate that the performance of the proposed framework is superior to representative state-of-the-art methods.

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