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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)
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 misclassified as object points causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal(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 shadow(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)
Vision-based systems for traffic surveillance have an impressive spread both for their practical application and interest as research issue. The most common approach used for vision-based traffic surveillance consists of a fast segmentation of Moving Visual Objects (MVOs) in the scene together with an intelligent reasoning module capable of identifying,(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 into(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)
This paper presents a novel and robust approach to consistent labeling for people surveillance in multicamera systems. A general framework scalable to any number of cameras with overlapped views is devised. An offline training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one(More)
This paper describes a system for motion detection based on background suppression,specifically conceived for working in complex scenes with vacillating background,camouflage, illumination changing, etc.. The system contains proper techniques for background bootstrapping, shadow removal, ghost suppression and selective updating of the background model. The(More)
a r t i c l e i n f o Keywords: Pick-and-place applications Machine vision for industrial applications SIFT This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach(More)