Multiple Object Tracking via Feature Pyramid Siamese Networks

IEEE Access

Sangyun Lee¹  Euntai Kim¹ 

[1] School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea;

关键词: Discriminative feature learning;    feature pyramid network (FPN);    multiple object tracking (MOT);    Siamese network;    similarity metric learning;   

DOI  :  10.1109/ACCESS.2018.2889442

来源: DOAJ

【 摘 要 】

When multiple object tracking (MOT) based on the tracking-by-detection paradigm is implemented, the similarity metric between the current detections and existing tracks plays an essential role. Most of the MOT schemes based on a deep neural network learn the similarity metric using a Siamese architecture, but the plain Siamese architecture might not be enough owing to its structural simplicity and lack of motion information. This paper aims to propose a new MOT scheme to overcome the existing problems in the conventional MOTs. Feature pyramid Siamese network (FPSN) is proposed to address the structural simplicity. The FPSN is inspired by a feature pyramid network (FPN) and it extends the Siamese network by applying FPN to the plain Siamese architecture and by developing a new multi-level discriminative feature. A spatiotemporal motion feature is added to the FPSN to overcome the lack of motion information and to enhance the performance in MOT. Thus, FPSN-MOT considers not only the appearance feature but also motion information. Finally, FPSN-MOT is applied to the public MOT challenge benchmark problems and its performance is compared to that of the other state-of-the-art MOT methods.

【 授权许可】

Unknown