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.
|Titolo:||Fast but Not Deep: Efficient Crowd Abnormality Detection with Local Binary Tracklets|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||04.01 - Contributo in atti di convegno|