Cast Shadow Detection Research Articles

An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal

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, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets.
 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 publicly common datasets that the performance of the proposed framework is superior to representative state-of-the-art methods.

A new cast shadow detection method for traffic surveillance video analysis using color and statistical modeling

In traffic surveillance video analysis systems, the cast shadows of vehicles often have a negative effect on video analysis results. A novel cast shadow detection framework, which consists of a new foreground detection method and a cast shadow detection method, is presented in this paper to detect and remove the cast shadows from the foreground. The new foreground detection method applies an innovative Global Foreground Modeling (GFM) method, a Gaussian mixture model or GMM, and the Bayes classifier for foreground and background classification. While the GFM method is for global foreground modeling, the GMM is for local background modeling, and the Bayes classifier applies both the foreground and the background models for foreground detection. The rationale of the GFM method stems from the observation that the foreground objects often appear in recent frames and their trajectories often lead them to different locations in these frames. As a result, the statistical models used to characterize the foreground objects should not be pixel based or locally defined. The cast shadow detection method contains four hierarchical steps. First, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the HSV color space. Second, a new shadow region detection method is proposed to cluster the candidate shadow pixels into shadow regions. Third, a statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented for classifying shadow pixels. Fourth, an aggregated shadow detection method is presented for final shadow detection. Experiments using the public video data ‘Highway-1’ and ‘Highway-3’, and the New Jersey Department of Transportation (NJDOT) real traffic video sequences show the feasibility of the proposed method. In particular, the proposed method achieves better shadow detection performance than the popular shadow detection methods, and is able to improve the traffic video analysis results.

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

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.

Cast shadow detection based on the YCbCr color space and topological cuts

In order to solve the moving objects shadow problem in foreground extraction of surveillance video images, a new cast shadow detection algorithm based on the YCbCr color space and topological cutting was proposed. Preliminary shadow removal was first performed based on the difference of the three components in the YCbCr color space of shadows and foregrounds. Besides, the maximum-flow/minimum-cut algorithm for image segmentation was optimized considering the topological constraints. The optimal segmentation of the foreground image was obtained during the continuous updating of the label. Finally, two sets of experiments were performed in video image sequences, including real surveillance videos and a well-known benchmark test set. By comparing with two other existing algorithms, the feasibility and effectiveness of the cast shadow detection algorithm were verified by the smooth border and higher recognition accuracy. In addition, the adaptability to foreground object density and different light intensities was measured in an airport terminal, showing that this algorithm can provide a high quality of moving foreground detection in surveillance video images and can be applied in monitoring of public places.

Moving cast shadow detection using scale-relation multi-layer pooling features

Moving cast shadows detection and removal are indispensable for object detection and are the problems in visual surveillance applications which have been studied over the years. However, finding an efficient model that can handle the issue of moving cast shadow in various situations is still challenging. Unlike prior methods, we use a data-driven method without strong parametric assumptions or complex models to address the problem of moving cast shadow. In this paper, we propose a novel feature-extracting framework called Scale-Relation Multi-Layer Pooling Feature Extracting (SMPF) which includes two main tasks: (1) Scale-Relation Scheme (SRS), (2) Multi-Layer Pooling Scheme (MLPS). By leveraging the scale space, SRS firstly decomposes feature images of each shadow properties into various scales and further considers the relationship between adjacent scaled feature images of each shadow properties to extract the scale-relation features. Then, we design the multi-layer pooling scheme (MLPS) to integrate the features in a local region and to reduce the dimension of extracting features. After that, the density map is generated for various properties of shadow with low dimension. Finally, to seek the criteria for discriminating moving cast shadow, we use random forest algorithm as the ensemble decision scheme. The main contributions of this study are (1) we design the features with multi-scale which can provide abundant information to describe the moving cast shadow, (2) the multi-layer pooling scheme generates the density map to integrate and reduce the dimensions of features. Experiments on the popular benchmarks and the proposed dataset with benchmarks demonstrate that the proposed method can achieve the performances of the popular methodologies.

Cast Shadow Detection to Quantify the Aerosol Optical Thickness for Atmospheric Correction of High Spatial Resolution Optical Imagery

The atmospheric correction of optical remote sensing data requires the determination of aerosol and gas optical properties. A method is presented which allows the detection of the aerosol scattering effects from optical remote sensing data at spatial sampling intervals below 5 m in cloud-free situations from cast shadow pixels. The derived aerosol optical thickness distribution is used for improved atmospheric compensation. In a first step, a novel spectral cast shadow detection algorithm determines the shadow areas using spectral indices. Evaluation of the cast shadow masks shows an overall classification accuracy on an 80% level. Using the such derived shadow map, the ATCOR atmospheric compensation method is iteratively applied on the shadow areas in order to find the optimum aerosol amount. The aerosol optical thickness is found by analyzing the physical atmospheric correction of fully shaded pixels in comparison to directly illuminated areas. The shadow based aerosol optical thickness estimation method (SHAOT) is tested on airborne imaging spectroscopy data as well as on photogrammetric data. The accuracy of the reflectance values from atmospheric correction using the such derived aerosol optical thickness could be improved from 3–4% to a level of better than 2% in reflectance for the investigated test cases.

Moving cast shadow detection using block nonnegative matrix factorization

Moving cast shadow detection using block nonnegative matrix factorization

Improving shadows detection for solar radiation numerical models

Solar radiation numerical models need the implementation of an accurate method for determining cast shadows on the terrain or on solar collectors. The aim of this work is the development of a new methodology to detect the shadows on a particular terrain. The paper addresses the detection of self and cast shadows produced by the orography as well as those caused by clouds. The paper presents important enhancements on the methodology proposed by the authors in previous works, to detect the shadows caused by the orography. The domain is the terrain surface discretised using an adaptive mesh of triangles. A triangle of terrain will be under cast shadows when, looking at the mesh from the Sun, you can find another triangle that covers all or partially the first one. For each time step, all the triangles should be checked to see if there are cast or self shadows on it. The computational cost of this procedure eventually resulted unaffordable when dealing with complex topography such as that in Canary Islands thus, a new methodology was developed. This one includes a filtering system to identify which triangles are those likely to be shadowed. If there are no self shadowed triangles, the entire mesh will be illuminated and there will not be any shadows. Only triangles that have their backs towards the Sun will be able to cast shadows on other triangles. Detection of shadows generated by clouds is achieved by a shadow algorithm using satellite images. In this paper, Landsat 8 images have been used. The code was done in python programming language. Finally, the outputs of both approaches, shadows generated by the topography and generated by clouds, can be combined in one map. The whole problem has been tested in Gran Canaria and Tenerife Island (Canary Islands – Spain), and in the Tatra Mountains (Poland and Slovakia).

Online sequential extreme learning machine-based co-training for dynamic moving cast shadow detection

Cast shadow detection and removal is one of the key problems in vision-based systems for accurate and robust segmentation of moving objects. This paper proposes a co-training-based adaptive method for detecting moving shadows in video sequences. Shadow detection based on static methods cannot adapt to changing environment such as gradual illumination changes. In order to solve this problem, we have proposed an online sequential extreme learning machine (OS-ELM)-based semi-supervised technique for moving cast shadow detection. Online learning of OS-ELM is much faster and provides better generalization performance compared to other popular online learning algorithms. First, we extracted color, texture, gradient, and image patch similarity features using a background model and input video frame, which are useful for discriminating moving shadows and objects. Co-training scheme is used for online updating of the OS-ELM classifier in order to adapt to the dynamic environment. Experimental results on different benchmark video sequences shows that the proposed method performs better shadow detection and discrimination compared with other methods.

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

In the process of segmentation and tracking of moving object, shadow area leads to false detection of object. Shadows are also reason for loss of background texture and false connectivity of independent blobs. Hence, we propose a simple method to detect a moving object’s cast shadow and remove the shadow region from video frames. Initially, we store all the background information in reference frame. The next incoming frames with object are compared with this frame. In order to extract the moving object, we used subtraction algorithm. We used homogeneity property by image division, color variation in RGB color space and statistics of intensity in V channel of HSV color space to detect the shadow region. Finally shadow removal is done based on the information from the reference frame. The most noticeable feature of our proposed method is that it can detect shadows both in indoor and outdoor scenarios and there is no harsh transition after removal of the shadow. Color information for both background subtraction and shadow detection to improve object segmentation is ensured in this paper. Experimental results show that the proposed method is simple to understand, can detect and remove shadow and extract the moving object properly.

지능 영상 감시를 위한 흑백 영상 데이터에서의 효과적인 이동 투영 음영 제거

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis

In this paper, we propose an adaptive and accurate moving cast shadow detection method employing online sub-scene shadow modeling and object inner-edges analysis for applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. The online shadow models are learned by utilizing Gaussian functions to fit the significant peaks of accumulating histograms, which are calculated from Hue, Saturation and Intensity (HSI) difference of moving objects between background and foreground. Additionally, object inner-edges analysis is adopted to reject camouflages, which are misclassified foreground regions that are highly similar to shadows. Finally, the main shadow regions are expanded to recycle the misclassified shadow pixels based on local color constancy. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.

Cast shadow detection for moving objects based on binocular stereo vision

A novel cast shadow detection approach was proposed. A stereo vision system was used to capture images instead of traditional single camera. It was based on an assumption that cast shadows were on a special plane. The image obtained from one camera was inversely projected to the plane and then transformed to the view from another camera. The points on the plane shared the same position between original image and the transformed image. As a result, the cast shadows can be detected. In order to improve the efficiency of cast shadow detection and decrease computational complexity, the obvious object areas in CIELAB color space were removed and the potential shadow areas were obtained. Experimental results demonstrate that the proposed approach can detect cast shadows accurately even under various illuminations.

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

Cascaded cast shadow detection method in surveillance scenes

Moving cast shadow detection is an active research field with improving the performance of moving object detection. In this paper, we propose a novel cast shadow detection method by exploiting the local texture similarity and the intensity ratio effectively between the shadow and background patches. Firstly, local texture analysis of the initial moving patches is carried out using local binary patterns, and a part of moving object pixels and shadow pixels will be detected exactly. Then, the residual moving pixels are classified by the intensity ratio. Consequently, moving shadow pixels and moving object pixels are located respectively. Specially, post processing is employed to adjust error pixels acquired through the first two steps. In order to prove the efficiency and feasibility, comparable experiments are performed and the results validate that it can detect the shadow pixels more accurately and completely than some state-of-the-art methods.

Robust and accurate moving shadow detection based on multiple features fusion

Abstract In recent years, moving cast shadow detection has been becoming a critical challenge to improve the accuracy of moving object detection in video surveillance. In this paper, we derive a robust moving cast shadow detection method based on multiple features fusion. Firstly, several kinds of features such as intensity, color and texture are extracted sufficiently by means of various measures for the foreground image. Then, the synthetic feature map is generated by linear combination of these features. Consequently, moving cast shadow pixels are distinguished from their moving objects roughly. Finally, spatial adjustment is applied to correct misclassified pixels for acquiring the refined shadow detection result. The effectiveness of our proposed method is evaluated on various scenes. The results demonstrate that the method can achieve high detection rate. In particular, the experiments also indicate that it significantly outperforms several state-of-the-art methods by extensive comparisons.

Integrated moving cast shadows detection method for surveillance videos

Moving cast shadow detection plays an exceedingly important role in content-based analysis in surveillance videos. A novel integrated moving cast shadow detection method is proposed for accurate moving objects detection. Based on the analysis for shadow model, we first exploit color information sufficiently in hue, saturation, and value color space and texture similarity in texture space to separate moving cast shadows from the foreground, respectively. Then we obtain the rough shadow detection result by means of synthesizing the above two results efficiently. Finally, post processing is put forward to modify misclassified pixels for acquiring the refined shadow detection result. Extensive experiments and comparisons of various scenes prove that the proposed method can detect cast shadows automatically and effectively and outperforms some well-known methods.

A survey of cast shadow detection algorithms

Cast shadows need careful consideration in the development of robust dynamic scene analysis systems. Cast shadow detection is critical for accurate object detection in video streams, and their misclassification can cause errors in segmentation and tracking. Many algorithms for shadow detection have been proposed in the literature; however a complete, comparative evaluation of existing approaches is lacking. This paper presents a comprehensive survey of shadow detection methods, organised in a novel taxonomy based on object/environment dependency and implementation domain. In addition a comparative evaluation of representative algorithms, based on quantitative and qualitative metrics is presented to evaluate the algorithms on a benchmark suite of indoor and outdoor video sequences.

Shadow detection: A survey and comparative evaluation of recent methods

This paper presents a survey and a comparative evaluation of recent techniques for moving cast shadow detection. We identify shadow removal as a critical step for improving object detection and tracking. The survey covers methods published during the last decade, and places them in a feature-based taxonomy comprised of four categories: chromacity, physical, geometry and textures. A selection of prominent methods across the categories is compared in terms of quantitative performance measures (shadow detection and discrimination rates, colour desaturation) as well as qualitative observations. Furthermore, we propose the use of tracking performance as an unbiased approach for determining the practical usefulness of shadow detection methods. The evaluation indicates that all shadow detection approaches make different contributions and all have individual strength and weaknesses. Out of the selected methods, the geometry-based technique has strict assumptions and is not generalisable to various environments, but it is a straightforward choice when the objects of interest are easy to model and their shadows have different orientation. The chromacity based method is the fastest to implement and run, but it is sensitive to noise and less effective in low saturated scenes. The physical method improves upon the accuracy of the chromacity method by adapting to local shadow models, but fails when the spectral properties of the objects are similar to that of the background. The small-region texture based method is especially robust for pixels whose neighbourhood is textured, but may take longer to implement and is the most computationally expensive. The large-region texture based method produces the most accurate results, but has a significant computational load due to its multiple processing steps.

Segmentation Approach Methods in Complex Environment

Segmentation is fundamental step in video surveillance system. It is hard to segmented interesting object in all environments due to the similarity features of objects and background. The segmentation results (foregrounds) are often broken. An approach composition of some methods including GMM, removing cast shadow, clustering and segmentation broken detection and recovery is proposed to improve the segmentation results. This method is suitable for the applications that the information of bounding boxes is required.