Detecting Moving Objects, Ghosts and Shadows in Video Streams

Detecting Moving Objects, Ghosts and Shadows in Video Streams (2003)

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by Rita Cucchiara , Costantino Grana , Massimo Piccardi , Andrea Prati

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	87 - 13 self

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Datum	Value	Source
TITLE	Detecting Moving Objects, Ghosts and Shadows in Video Streams	INFERENCE
AUTHOR NAME	Rita Cucchiara	SVM HeaderParse 0.2
AUTHOR NAME	Costantino Grana	SVM HeaderParse 0.2
AUTHOR NAME	Massimo Piccardi	SVM HeaderParse 0.2
AUTHOR NAME	Andrea Prati	SVM HeaderParse 0.2
ABSTRACT	Abstract—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 approaches. This work proposes a general-purpose method that combines statistical assumptions with the objectlevel knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes. Index Terms—Background modeling, color segmentation, reactivity to changes, shadow detection, video surveillance, object-level knowledge. 1	SVM HeaderParse 0.2
YEAR	2003	INFERENCE
VENUE	IEEE Transactions on Pattern Analysis and Machine Intelligence	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	1337--1342	INFERENCE
VOLUME	25	INFERENCE
CITATIONS	19 found	ParsCit 1.0