Abnormal High-Density Crowd Dataset

Abstract

Anomaly detection within crowded environments is a key challenge in the computer vision and crowd behaviour understanding fields. Furthermore, anomaly detection within high-density crowds remains an insufficiently explored area. In this paper, we propose a novel abnormal high-density crowd dataset. The proposed dataset adheres to the same constraints as some of the benchmark datasets such as UCSD, UMN and Avenue dataset. These constraints include occurrences of both normal and abnormal behaviour and validated abnormal behaviour annotations. Additionally, this dataset includes footage of only high-density crowds, whereas benchmark datasets include low to medium density crowds. We have taken into consideration privacy issues, the veracity of annotations and pre-processing of the dataset. We evaluate the dataset against state-of-the-art crowd anomaly detection methods. The generated results indicate that training/testing these methods on high-density crowds decreases their detection performance.