Notice of Removal: Novel dataset for fine-grained abnormal behavior understanding in crowd
Hamidreza Rabiee, Javad Haddadnia, Hossein Mousavi, Maziyar Kalantarzadeh, Moin Nabi, Vittorio Murino · 2016
Despite the huge research on crowd on behavior understanding in visual surveillance community, lack of publicly available realistic datasets for evaluating crowd behavioral interaction led not to have a fair common test bed for researchers to compare the strength of their methods in the real scenarios. This work presents a novel crowd dataset contains around 45,000 video clips which annotated by one of the five different fine-grained abnormal behavior categories. We also evaluated two state-of-the-art methods on our dataset, showing that our dataset can be effectively used as a benchmark for fine-grained abnormality detection. The details of the dataset and the results of the baseline methods are presented in the paper.