Event classification for automatic visual-based surveillance of parking lots

被引：20

作者：

Foresti, GL ^{[1
]}

Micheloni, C ^{[1
]}

Snidaro, L ^{[1
]}

机构：

[1] Univ Udine, Dept Math & Comp Sci, DIMI, I-33100 Udine, Italy

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3 | 2004年

关键词：

neural trees; object classification; event recognition; surveillance systems;

D O I：

10.1109/ICPR.2004.1334530

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a visual-based surveillance system for real-time event detection and classification in parking lots is presented. The focus is on the high-level part of the system, i.e., the event recognition (ER) module, which is able to analyze two kinds of events (i.e., simple and composite events) that occur in the observed scene. Simple events are represented by single moving objects, e.g., vehicles, pedestrians, etc. while a composite event is represented by a set of temporally consecutive simple events, e.g., people exiting a car just entered in the parking area. An adaptive high order neural tree (AHNT) is applied for recognizing both objects and complex events.

引用

页码：314 / 317

页数：4

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