VisLoiter+: An Entropy Model-Based Loiterer Retrieval System with User-Friendly Interfaces
Title | VisLoiter+: An Entropy Model-Based Loiterer Retrieval System with User-Friendly Interfaces |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Sandifort, Maguell L.T.L., Liu, Jianquan, Nishimura, Shoji, Hürst, Wolfgang |
Conference Name | Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-5046-4 |
Keywords | entropy model, heatmap, Human Behavior, loiterer retrieval, loitering discovery, Metrics, pubcrawl, ranking system, Resiliency, video surveillance |
Abstract | It is very difficult to fully automate the detection of loitering behavior in video surveillance, therefore humans are often required for monitoring. Alternatively, we could provide a list of potential loiterer candidates for a final yes/no judgment of a human operator. Our system, VisLoiter+, realizes this idea with a unique, user-friendly interface and by employing an entropy model for improved loitering analysis. Rather than using only frequency of appearance, we expand the loiter analysis with new methods measuring the amount of person movements across multiple camera views. The interface gives an overview of loiterer candidates to show their behavior at a glance, complemented by a lightweight video playback for further details about why a candidate was selected. We demonstrate that our system outperforms state-of-the-art solutions using real-life data sets. |
URL | https://dl.acm.org/citation.cfm?doid=3206025.3206091 |
DOI | 10.1145/3206025.3206091 |
Citation Key | sandifortVisLoiterEntropyModelBased2018 |