Trajectory-Based Surveillance Analysis: A Survey

Due to the advancement of camera hardware and machine learning techniques, video object tracking for surveillance has received noticeable attention from the computer vision research community. Object tracking and trajectory modeling have important applications in surveillance video analysis. For exa...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2019-07, Vol.29 (7), p.1985-1997
Main Authors: Ahmed, Sk. Arif, Dogra, Debi Prosad, Kar, Samarjit, Roy, Partha Pratim
Format: Article
Language:English
Subjects:
Activity recognition
Anomalies
anomaly detection
Clustering
Communities
Computer vision
Feature extraction
Machine learning
Object tracking
Surveillance
Surveillance video analysis
Target tracking
Tracking
Trajectory
Trajectory analysis
Video data
video summarization
video synopsis
Visualization
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Summary:Due to the advancement of camera hardware and machine learning techniques, video object tracking for surveillance has received noticeable attention from the computer vision research community. Object tracking and trajectory modeling have important applications in surveillance video analysis. For example, trajectory clustering, summarization or synopsis generation, and detection of anomalous or abnormal events in videos are mainly being exploited by the research community. However, barring one research work (which is almost a decade old), there is no recent review that emphasizes the use of video object trajectories, particularly in the perspective of visual surveillance. This paper presents a survey of trajectory-based surveillance applications with a focus on clustering, anomaly detection, summarization, and synopsis generation. The methods reviewed in this paper broadly summarize the abovementioned applications. The main purpose of this survey is to summarize the state-of-the-art video object trajectory analysis techniques used in the indoor and outdoor surveillance.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2018.2857489