MoRe: A Large-Scale Motorcycle Re-Identification Dataset
Motorcycles are often related to transit and criminal issues due to its abundance in the transit. Despite its importance, motorcycles are a seldom addressed problem in the computer vision community. We credit this problem to the lack of large-scale datasets and strong baseline models. Therefore, we present the first large-scale Motorcycles Re-Identification (MoRe) dataset. MoRe consists of 3,827 individuals (i.e., the set of motorbikes and motorcyclist) captured by ten surveillance cameras placed in Brazil's urban traffic scenarios. Furthermore, we evaluate a deep learning model trained using well-known training tricks from the object re-identification literature to present a strong baseline for the motorcycle re-identification (ReID) problem. More importantly, we highlight some crucial problems in this topic as the influence of distractors and the domain shift. Experimental results demonstrate the effectiveness of the strong baseline model with an increase of at least 19.27 p.p. in the rank-1 when compared to the state-of-the-art in the BPReID dataset. Finally, we present some insights regarding the information learned by the strong baseline model when computing the similarities between motorcycle images.