Insignificant shadow detection for video segmentation

To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance

Effective shadow detection in traffic monitoring applications

This paper presents work we have done in detecting moving shadows in the context of an outdoor traffic scene for visual surveillance purposes. The algorithm just exploits some foreground photometric properties concerning shadows. The input of the system is constituted by the blobs previously detected and by the division image between the current frame and the background of the scene. The method proposed is essentially based on multi-gradient operations applied on the division image which aim to discover the most likely shadow regions. Further on, the subsequent “smart” binary edge matching we devised is performed on each blob’s boundary and permits to effectively discard those regions inside the blob which are either too far from the boundary or too small. We demonstrate the effectiveness of our method by using a gray level sequence taken from a sunny, daytime, traffic scene. Since no a priori knowledge is used in order to detect, and remove, shadows, this method represents one of the most general purpose systems to date for detecting outdoor shadows

Vision-based traffic surveys in urban environments

This paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.The authors gratefully acknowledge the Royal Borough of Kingston for providing the video data. S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander

Moving object detection unaffected by cast shadows, highlights and ghosts

IEEE Copyright Policies: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.This paper describes a new approach to perform segmentation of moving objects in real-time from images acquired by a fixed color video camera and is the first tool of a major project that aspires to recognize abnormal human behavior in public areas. The moving objects detection is based on background subtraction and it is unaffected by changes in illumination, i.e., cast shadows and highlights. Furthermore it does not require a special attention during the initialization process, due to its ability to detect and rectify ghosts. The results show that with image resolutions of 380x280 at 24 bits per pixel, the time spent in the segmentation process is around 80ms, in a 32 bits 3GHz processor based computer.Fundação para a Ciência e a Tecnologia (FCT