Robust Event Detection and Retrieval in Surveillance Video
type: CreativeWork
description: We developed a robust event detection and retrieval system for surveillance video. The proposed system offers vision-based capabilities for the detection and tracking of various objects of interest, and can recognize events such as: 1. a person with certain attributes being present in the scene; 2. two people meeting; 3. people carrying bags; 4. bags being dropped; 5. bags being stolen; 6. bags being exchanged; 7. two people handshaking; 8. one person's pointing gesture. We use an improved adaptive Gaussian mixture model for background modeling and foreground detection; a connected component labeling algorithm is then employed to label the foreground pixels. A Kalman filter approach is used to build models for the entities of interest (people and bags), which is combined with color histograms for tracking. We use shape symmetry analysis and color histograms to detect people carrying bags. Our experiments demonstrate the ability to search for instances of events according to specific attributes in large video sequences.
abstract: We developed a robust event detection and retrieval system for surveillance video. The proposed system offers vision-based capabilities for the detection and tracking of various objects of interest, and can recognize events such as: 1. a person with certain attributes being present in the scene; 2. two people meeting; 3. people carrying bags; 4. bags being dropped; 5. bags being stolen; 6. bags being exchanged; 7. two people handshaking; 8. one person's pointing gesture. We use an improved adaptive Gaussian mixture model for background modeling and foreground detection; a connected component labeling algorithm is then employed to label the foreground pixels. A Kalman filter approach is used to build models for the entities of interest (people and bags), which is combined with color histograms for tracking. We use shape symmetry analysis and color histograms to detect people carrying bags. Our experiments demonstrate the ability to search for instances of events according to specific attributes in large video sequences.
author: Jiang, Jin
date published: 2014
date modified: 2026-07-27T21:48:09Z
provider: University of Nevada - Reno
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Nevada - Reno

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11714/2875