Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/AVSS.2018.8639172 | 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
Keywords | Field | DocType |
Tracking,Training,Histograms,Support vector machines,Video sequences,Three-dimensional displays,Binary codes | Computer vision,Histogram,Pattern recognition,Computer science,Binary code,Support vector machine,Abnormality,Artificial intelligence,Abnormality detection,Binary number | Conference |
ISBN | Citations | PageRank |
978-1-5386-9294-3 | 2 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahdyar Ravanbakhsh | 1 | 51 | 2.83 |
| Hossein Mousavi | 2 | 6 | 0.74 |
| Moin Nabi | 3 | 134 | 14.02 |
| Lucio Marcenaro | 4 | 401 | 66.21 |
| Carlo S. Regazzoni | 5 | 609 | 101.09 |