Lost in Time: Temporal Analytics for Long-Term Video Surveillance

Huai-Qian Khor*, John See

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis. With the abundance of video data captured over a long period of time, we can understand patterns in human behavior and scene dynamics through data-driven temporal analytics. In this work, we propose two schemes to perform descriptive and predictive analytics on long-term video surveillance data. We generate heatmap and footmap visualizations to describe spatially pooled trajectory patterns with respect to time and location. We also present two approaches for anomaly prediction at the day-level granularity: a trajectory-based statistical approach, and a time-series based approach. Experimentation with one year data from a single camera demonstrates the ability to uncover interesting insights about the scene and to predict anomalies reasonably well.

Original languageEnglish
Title of host publicationComputational Science and Technology. ICCST 2017
EditorsRayner Alfred, Hiroyuki Iida, Asri Ag. Ibrahim, Yuto Lim
Number of pages11
ISBN (Electronic)9789811082764
ISBN (Print)9789811082757
Publication statusPublished - 24 Feb 2018
Event4th International Conference on Computational Science and Technology 2017 - Kuala Lumpur, Malaysia
Duration: 29 Nov 201730 Nov 2017

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference4th International Conference on Computational Science and Technology 2017
Abbreviated titleICCST17
CityKuala Lumpur

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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