Modern automated visual surveillance scenarios demand to process effectively a large set of visual stream with a limited amount of human resources. Actionable information is required in real-time, therefore abnormal pattern detection shall be performed in order to select the most useful streams for an operator to visually inspect. To tackle this challenging task we propose a novel method based on convex polytope ensembles to perform anomaly detection. Our method relies on local trajectory based features. We report State-of-the-Art results on pixel-level anomaly detection on the challenging publicly available UCSD Pedestrian dataset.

Convex polytope ensembles for spatio-temporal anomaly detection / Turchini, Francesco; Seidenari, Lorenzo; Del Bimbo, Alberto. - ELETTRONICO. - 10484:(2017), pp. 174-184. (Intervento presentato al convegno 19th International Conference on Image Analysis and Processing, ICIAP 2017 tenutosi a ita nel 2017) [10.1007/978-3-319-68560-1_16].

Convex polytope ensembles for spatio-temporal anomaly detection

Turchini, Francesco;Seidenari, Lorenzo
;
Del Bimbo, Alberto
2017

Abstract

Modern automated visual surveillance scenarios demand to process effectively a large set of visual stream with a limited amount of human resources. Actionable information is required in real-time, therefore abnormal pattern detection shall be performed in order to select the most useful streams for an operator to visually inspect. To tackle this challenging task we propose a novel method based on convex polytope ensembles to perform anomaly detection. Our method relies on local trajectory based features. We report State-of-the-Art results on pixel-level anomaly detection on the challenging publicly available UCSD Pedestrian dataset.
2017
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
19th International Conference on Image Analysis and Processing, ICIAP 2017
ita
2017
Turchini, Francesco; Seidenari, Lorenzo; Del Bimbo, Alberto
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1104784
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