In this paper, an efficient method for crowd abnormal behavior detection and localization is introduced. Despite the significant improvements of deep-learning-based methods in this field, but still, they are not fully applicable for the real-time applications. We propose a simple yet effective descriptor based on binary tracklets, containing both orientation and magnitude information in a single feature. The results of the proposed method are comparable with deep-based methods while it performs more efficiently. The evaluation of our descriptors on three different datasets yields a promising result in abnormality detection, which is competitive with the state-of-the-art methods.

Fast but Not Deep: Efficient Crowd Abnormality Detection with Local Binary Tracklets

Ravanbakhsh, Mahdyar;MOUSAVI, SEYYED HOSSEIN;Nabi, Moin;Marcenaro, Lucio;Regazzoni, Carlo
2018

Abstract

In this paper, an efficient method for crowd abnormal behavior detection and localization is introduced. Despite the significant improvements of deep-learning-based methods in this field, but still, they are not fully applicable for the real-time applications. We propose a simple yet effective descriptor based on binary tracklets, containing both orientation and magnitude information in a single feature. The results of the proposed method are comparable with deep-based methods while it performs more efficiently. The evaluation of our descriptors on three different datasets yields a promising result in abnormality detection, which is competitive with the state-of-the-art methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/942456
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