AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes

by   Yunhao Li, et al.

Multi-object 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 Re-ID embedding. We first introduce AttMOT, a large, highly enriched synthetic dataset for pedestrian tracking, containing over 80k frames and 6 million pedestrian IDs with different time, weather conditions, and scenarios. To the best of our knowledge, AttMOT is the first MOT dataset with semantic attributes. Subsequently, we explore different approaches to fuse Re-ID embedding and pedestrian attributes, including attention mechanisms, which we hope will stimulate the development of attribute-assisted MOT. The proposed method AAM demonstrates its effectiveness and generality on several representative pedestrian multi-object tracking benchmarks, including MOT17 and MOT20, through experiments on the AttMOT dataset. When applied to state-of-the-art trackers, AAM achieves consistent improvements in MOTA, HOTA, AssA, IDs, and IDF1 scores. For instance, on MOT17, the proposed method yields a +1.1 MOTA, +1.7 HOTA, and +1.8 IDF1 improvement when used with FairMOT. To encourage further research on attribute-assisted MOT, we will release the AttMOT dataset.


page 1

page 3

page 4

page 6

page 7

page 12


Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset

Understanding human-object interactions is fundamental in First Person V...

MOT20: A benchmark for multi object tracking in crowded scenes

Standardized benchmarks are crucial for the majority of computer vision ...

Real-time Pedestrian Surveillance with Top View Cumulative Grids

This manuscript presents an efficient approach to map pedestrian surveil...

RLM-Tracking: Online Multi-Pedestrian Tracking Supported by Relative Location Mapping

The problem of multi-object tracking is a fundamental computer vision re...

A Richly Annotated Dataset for Pedestrian Attribute Recognition

In this paper, we aim to improve the dataset foundation for pedestrian a...

Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking

Multi-object tracking (MOT) is an essential task in the computer vision ...

Hierarchical Feature Embedding for Attribute Recognition

Attribute recognition is a crucial but challenging task due to viewpoint...

Please sign up or login with your details

Forgot password? Click here to reset