Yunhao Li, Zhen Xiao, Lin Yang, Dan Meng, Xin Zhou, Heng Fan, Libo Zhang
Multiobject tracking (MOT) is a fundamental problem in computer vision with numerous applications, such as intelligent surveillance and automated driving. Despite the significant progress made in MOT, pedestrian attributes, such as gender, hairstyle, body shape, and clothing features, which contain rich and high-level information, have been less explored. To address this gap, we propose a simple, effective, and generic method to predict pedestrian attributes to support general reidentification (Re-ID) embedding...
April 25, 2024: IEEE Transactions on Neural Networks and Learning Systems