Remove Cast Shadows Research Articles

Moving shadow detection via binocular vision and colour clustering

A pedestrian segmentation algorithm in the presence of cast shadows is presented in this study. The novelty of this algorithm lies in the fusion of multi-view and multi-plane homographic projections of foregrounds and the use of the fused data to guide colour clustering. This brings about an advantage over the existing binocular algorithms in that it can remove cast shadows while keeping pedestrians’ body parts, which occlude shadows. Phantom detection, which is inherent with the binocular method, is also investigated. Experimental results with real-world videos have demonstrated the efficiency of this algorithm.

Adaptive moving shadow detection and removal by new semi-supervised learning technique

The efficient application of current methods of shadow detection in video is hindered by the difficulty in defining their parameters or models and/or their application domain dependence. This paper presents a new shadow detection and removal method that aims to overcome these inefficiencies. It proposes a semi-supervised learning rule using a new variant of co-training technique for shadow detection and removal in uncontrolled scenes. The new variant both reduces the run-time through a periodical execution of a co-training process according to a novel temporal framework, and generates a more generic prediction model for an accurate classification. The efficiency of the proposed method is shown experimentally on a testbed of videos that were recorded by a static camera and that included several constraints, e.g., dynamic changes in the natural scene and various visual shadow features. The conducted experimental study produced quantitative and qualitative results that highlighted the robustness of our shadow detection method and its accuracy in removing cast shadows. In addition, the practical usefulness of the proposed method was evaluated by integrating it in a Highway Control and Management System software called RoadGuard.

Moving Shadow Removal for Multi-Objects Tracking in Outdoor Environments

Shadow detection and removal has had great interest in computer vision especially in outdoor environments. It is an important task for visual tracking, object recognition, and many other important applications. One of the fundamental challenges for accurate tracking is achieving invariance to shadows. Two or more separate objects can appear to be connected through shadows. Many algorithms have been proposed in the literature that deal with shadows. However, the problem remains largely unsolved and needs further research effort. This paper proposes a method for removing cast shadows from vehicles in outdoor environments. The proposed method employs the estimated background model of the video sequence and applies a Gamma decoding followed by a thresholding operation. Experimental results show the success of the proposed method in detecting and removing shadows robustly and leads to considerable improvements in multiple object tracking.

An Efficient Hierarchical Approach for Background Subtraction and Shadow Removal using Adaptive GMM and Color Discrimination

paper presents an efficient approach for moving objects detection and shadow removal from color videos obtained using stationary camera. A background subtraction technique based on modified adaptive GMM has been proposed for detecting moving objects. Speed-up techniques have also been applied to enhance the computational efficiency of the algorithm. Then, a robust algorithm for shadow removal is used to remove cast shadows and ghosts. Foreground is reconstructed using graph cut based cleaning and non-recursive blob finding. Comparative experimental results demonstrate that proposed approach performs better in comparison to other state-of-the-art algorithms.

Background updating and shadow detection based on spatial, color, and texture information of detected objects

Background model updating is a vital process for any background subtraction technique. This paper presents an updating mechanism that can be applied efficiently to any background subtraction technique. This updating mechanism exploits the color and spatial features to characterize each detected object. Spatial and color features are used to classify each detected object as a moving background object, a ghost, or a real moving object. The starting position of each detected object is the cue for updating background images. In addition, this paper presents a hybrid scheme to detect and remove cast shadows based on texture and color features. The robustness of the proposed method and its effectiveness in overcoming challenging problems such as gradual and sudden illumination changes, ghost appearance, non-stationary background objects, the stability of moving objects most of the time, and cast shadows are verified quantitatively and qualitatively.

Robust object detection with real-time fusion of multiview foreground silhouettes

An object detection algorithm using multiple cameras is proposed. The information fusion is based on homography mapping of the foreground information from multiple cameras and for multiple parallel planes. Unlike the most recent algorithms, which transmit and project foreground bitmaps, it approximates each foreground silhouette with a polygon and projects the polygon vertices only. In addition, an alternative approach to estimating the homographies for multiple parallel planes is presented. It is based on the observed pedestrians and does not resort to vanishing-point estimation. The ability of this algorithm to remove cast shadows in moving object detection is also investigated. The results on open video datasets are presented as well.

Improvement of Human Tracking in Stereoscopic Environment Using Subtraction Stereo with Shadow Detection

The combination of subtraction stereo with shadow detection we propose improves people tracking in stereoscopic environments. Subtraction stereo is a stereo matching method which is fast and robust for the correspondence problem – one of the most serious issues in computer vision – restricting the search range of matching to foreground regions. Shadow detection gives adequate foreground regions of tracked people by removing cast shadows. This leads to accurate three-dimensional measurement of positions in stereoscopic environment tracking. By focusing on disparity images obtained by subtraction stereo, we can detect people easily based on standard labeling. Objects can also be measured directly in size by subtraction stereo without geometric information about environments for tracking. This is important for installing the tracking system easily. To track multiple passing people, we use the extended Kalman filter to address the occlusion problem usually encountered in crowded environments. The proposed method is verified by experiments using unknown stereoscopic environments.

Learning and Removing Cast Shadows through a Multidistribution Approach

Moving cast shadows are a major concern for foreground detection algorithms. The processing of foreground images in surveillance applications typically requires that such shadows be identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of nonuniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, including light saturated areas, and prevent false detection in regions where shadows cannot be detected. The proposed approach can be used with pixel-based descriptions of shadowed surfaces found in the literature. It significantly reduces their false detection rate without increasing the missed detection rate. Results obtained with different scene types and shadow models show the robustness of the approach.

Investigation into Shadow Removal from Traffic Images

Traffic surveillance cameras are becoming a viable replacement for inductive loop detectors. The effectiveness of these cameras, however, depends on video image processing algorithms that can alleviate common problems such as shadows, vehicle occlusion, reflection, and camera shake. Shadows have proved to be a major source of error in the detection and classification of vehicles. Three algorithms of increasing complexity are proposed to address the shadow problem. The algorithms each address the need to remove cast shadows from vehicles while preserving self-shadows, or those areas of a vehicle that are hidden from illumination. They are also geared toward real-time analysis, which requires that they can be implemented efficiently and cannot have complex training or learning requirements. The dual-pass Otsu method of shadow removal was the simplest in application but had the poorest performance. The proposed region growing technique, though showing considerable promise, failed when the pixel intensity varied widely in the shadow region. The last technique used edge imaging to recognize shadows as areas with few edges or with edges substantially similar to the background. This method clearly outperformed the other methods and was subsequently proved in a separate paper describing a prototype vehicle detection and classification system.