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—Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such(More)
1 Abstract—Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams, since shadow points are often mis-classified as object points causing errors in segmentation and tracking. Many algorithms have been proposed in the literature(More)
— Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques able to extract, manage and track objects in the scene. However , problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for(More)
Robustness to changes in illumination conditions as well as viewing perspectives is an important requirement for many computer vision applications. One of the key factors in enhancing the robustness of dynamic scene analysis is that of accurate and reliable means for shadow detection. Shadow detection is critical for correct object detection in image(More)
Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challenging features of such approaches. This work presents a(More)
— Shadow detection is critical for robust and reliable vision-based systems for traffic flow analysis. In this paper we discuss various shadow detection approaches and compare two critically. The goal of these algorithms is to prevent moving shadows being misclassified as moving objects (or parts of them), thus avoiding the merging of two or more objects(More)
This paper presents a novel and robust approach to consistent labeling for people surveillance in multi-camera systems. A general framework scalable to any number of cameras with overlapped views is devised. An off-line training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one(More)
The analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. This paper proposes to model the shape of a single trajectory as a sequence of angles described using a Mixture of Von Mises (MoVM) distribution. A complete EM(More)
In this paper, we describe an integrated solution devised for In-House Video Surveillance, to control the safety of people living in a domestic environment. The system is composed of a robust moving object detection module, able to disregard shadows, a tracking module designed for large occlusion solution and of a posture detector. Shadows, large occlusions(More)
In this work we present a framework for on-the-fly video transcoding that exploits computer vision-based techniques to adapt the Web access to the user requirements. The proposed transcoding approach aims at coping both with user bandwidth and resources capabilities, and with user interests in the video's content. We propose an object-based semantic(More)