Paper Info
Title
Nobody likes Mondays: foreground detection and behavioral patterns analysis in complex urban scenes
Abstract
Streams of images from large numbers of surveillance webcams are available via the web. The continuous monitoring of activities at different locations provides a great opportunity for research on the use of vision systems for detecting actors, objects, and events, and for understanding patterns of activity and anomaly in real-world settings. In this work we show how images available on the web from surveillance webcams can be used as sensors in urban scenarios for monitoring and interpreting states of interest such as traffic intensity. We highlight the power of the cyclical aspect of the lives of people and of cities. We extract from long-term streams of images typical patterns of behavior and anomalous events and situations, based on considerations of day of the week and time of day. The analysis of typia and atypia required a robust method for background subtraction. For this purpose, we present a method based on sparse coding which outperforms state-of-the-art works on complex and crowded scenes.
Year
DOI
Venue
2013
10.1145/2510650.2510653
ARTEMIS@ACM Multimedia
Keywords
Field
DocType
background subtraction,different location,robust method,images typical pattern,anomalous event,behavioral patterns analysis,foreground detection,continuous monitoring,cyclical aspect,great opportunity,complex urban scene,surveillance webcams,crowded scene
Background subtraction,Computer vision,Behavioral pattern,Time of day,Neural coding,Computer science,Traffic intensity,Foreground detection,Continuous monitoring,nobody,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
6
0.46
28
Authors
5
Name
Order
Citations
PageRank
Gloria Zen118810.38
John Krumm23954355.60
Nicu Sebe37013403.03
Eric Horvitz494021058.25
Ashish Kapoor51833119.72