NDLI: Crowd Anomaly Detection via Spatial Constraints and Meaningful Perturbation

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Crowd Anomaly Detection via Spatial Constraints and Meaningful Perturbation

Content Provider	MDPI
Author	Feng, Jiangfan Wang, Dini Zhang, Li
Copyright Year	2022
Description	Crowd anomaly detection is a practical and challenging problem to computer vision and VideoGIS due to abnormal events’ rare and diverse nature. Consequently, traditional methods rely on low-level reconstruction in a single image space, easily affected by unimportant pixels or sudden variations. In addition, real-time detection for crowd anomaly detection is challenging, and localization of anomalies requires other supervision. We present a new detection approach to learn spatiotemporal features with the spatial constraints of a still dynamic image. First, a lightweight spatiotemporal autoencoder has been proposed, capable of real-time image reconstruction. Second, we offer a dynamic network to obtain a compact representation of video frames in motion, reducing false-positive anomaly alerts by spatial constraints. In addition, we adopt the perturbation visual interpretation method for anomaly visualization and localization to improve the credibility of the results. In experiments, our results provide competitive performance across various scenarios. Besides, our approach can process 52.9–63.4 fps in anomaly detection, making it practical for crowd anomaly detection in video surveillance.
Starting Page	205
e-ISSN	22209964
DOI	10.3390/ijgi11030205
Journal	ISPRS International Journal of Geo-Information
Issue Number	3
Volume Number	11
Language	English
Publisher	MDPI
Publisher Date	2022-03-18
Access Restriction	Open
Subject Keyword	ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Artificial Intelligence Videogis Spatiotemporal Geospatial Artificial Intelligence Spatial Constraints Deep Learning
Content Type	Text
Resource Type	Article

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