An efficient method of cast shadow removal using multiple features

Features of images are often used for cast shadow removal. A technique based on using only a single feature cannot universally distinguish an object pixel from a shadow pixel of a video frame. On the other hand, the use of multiple features increases the computational cost of a shadow removal technique considerably. In this paper, an efficient yet simple method for cast shadow removal from video sequences with static background using multiple features isdeveloped. The basic idea of the proposed technique is that a simultaneous use of a small number of multiple features, if chosen judiciously, can reduce the similarity between object and shadow pixels without an excessive increase in the computational cost. Using the features of gray levels, color composition and gradients of foreground and background pixels, a method is devised to create a complete object mask. First, based on each of the three features, three individual shadow masks are constructed, from which three corresponding object masks are obtained through a simple subtraction operation. The object masks are then merged together to generate a single object mask. Each of the three shadow masks is created so as to cover as many shadow pixels as possible, even if it results in falsely including in them some of the object pixels. As a result, the subsequent object masks may lose some of these pixels. However, the object pixels missed by one of the object masks should be able to be recovered by at least one of the other two, since they are generated based on features complementary to the one used to construct the first one. The final object mask obtained through a logical OR operation of the three individual masks can, therefore, be expected to include most of the object pixels. The proposed method is applied to a number of video sequences. The simulation results demonstrate that the proposed method provides a mechanism for shadow removal that is superior to some of the recently proposed techniques without imparting an excessive computational cost

Shadow removal utilizing multiplicative fusion of texture and colour features for surveillance image

Automated surveillance systems often identify shadows as parts of a moving object which jeopardized subsequent image processing tasks such as object identification and tracking. In this thesis, an improved shadow elimination method for an indoor surveillance system is presented. This developed method is a fusion of several image processing methods. Firstly, the image is segmented using the Statistical Region Merging algorithm to obtain the segmented potential shadow regions. Next, multiple shadow identification features which include Normalized Cross-Correlation, Local Color Constancy and Hue-Saturation-Value shadow cues are applied on the images to generate feature maps. These feature maps are used for identifying and removing cast shadows according to the segmented regions. The video dataset used is the Autonomous Agents for On-Scene Networked Incident Management which covers both indoor and outdoor video scenes. The benchmarking result indicates that the developed method is on-par with several normally used shadow detection methods. The developed method yields a mean score of 85.17% for the video sequence in which the strongest shadow is present and a mean score of 89.93% for the video having the most complex textured background. This research contributes to the development and improvement of a functioning shadow eliminator method that is able to cope with image noise and various illumination changes

Segmentation of Moving Objects in Video Sequences with a Dynamic Background

Segmentation of objects from a video sequence is one of the basic operations commonly employed in vision-based systems. The quality of the segmented object has a profound effect on the performance of such systems. Segmentation of an object becomes a challenging problem in situations in which the background scenes of a video sequence are not static or contain the cast shadow of the object. This thesis is concerned with developing cost-effective methods for object segmentation from video sequences having dynamic background and cast shadows. A novel technique for the segmentation of foreground from video sequences with a dynamic background is developed. The segmentation problem is treated as a problem of classifying the foreground and background pixels of the frames of a sequence using the pixel color components as multiple features of the images. The individual features representing the pixel gray levels, hue and saturation levels are first extracted and then linearly recombined with suitable weights to form a scalar-valued feature image. Multiple features incorporated into this scalar-valued feature image allows to devise a simple classification scheme in the framework of a support vector machine classifier. Unlike some other data classification approaches for foreground segmentation, in which a priori knowledge of the shape and size of the moving foreground is essential, in the proposed method, training samples are obtained in an automated manner. The proposed technique is shown not to be limited by the number, patterns or dimensions of the objects. The foreground of a video frame is the region of the frame that contains the object as well as its cast shadow. A process of object segmentation generally results in segmenting the entire foreground. Thus, shadow removal from the segmented foreground is essential for object segmentation. A novel computationally efficient shadow removal technique based on multiple features is proposed. Multiple object masks, each based on a single feature, are constructed and merged together to form a single object mask. The main idea of the proposed technique is that an object pixel is less likely to be indistinguishable from the shadow pixels simultaneously with respect to all the features used. Extensive simulations are performed by applying the proposed and some existing techniques to challenging video sequences for object segmentation and shadow removal. The subjective and objective results demonstrate the effectiveness and superiority of the schemes developed in this thesis

An efficient method of cast shadow removal using multiple features

Features of images are often used for cast shadow removal. A technique based on using only a single feature cannot universally distinguish an object pixel from a shadow pixel of a video frame. On the other hand, the use of multiple features increases the computational cost of a shadow removal technique considerably. In this paper, an efficient yet simple method for cast shadow removal from video sequences with static background using multiple features isdeveloped. The basic idea of the proposed technique is that a simultaneous use of a small number of multiple features, if chosen judiciously, can reduce the similarity between object and shadow pixels without an excessive increase in the computational cost. Using the features of gray levels, color composition and gradients of foreground and background pixels, a method is devised to create a complete object mask. First, based on each of the three features, three individual shadow masks are constructed, from which three corresponding object masks are obtained through a simple subtraction operation. The object masks are then merged together to generate a single object mask. Each of the three shadow masks is created so as to cover as many shadow pixels as possible, even if it results in falsely including in them some of the object pixels. As a result, the subsequent object masks may lose some of these pixels. However, the object pixels missed by one of the object masks should be able to be recovered by at least one of the other two, since they are generated based on features complementary to the one used to construct the first one. The final object mask obtained through a logical OR operation of the three individual masks can, therefore, be expected to include most of the object pixels. The proposed method is applied to a number of video sequences. The simulation results demonstrate that the proposed method provides a mechanism for shadow removal that is superior to some of the recently proposed techniques without imparting an excessive computational cost