First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Atomic Activity Recognition 2024

TL;DR

This paper details the winning solution for the ECCV 2024 ROAD++ Challenge Track 3, combining multi-branch recognition, ensembling, and data augmentation to improve atomic activity recognition in road scene videos.

Contribution

The paper introduces a multi-branch framework, diverse ensembling strategies, and a novel data augmentation method to address challenges in atomic activity recognition.

Findings

Achieved 69% mAP on test set

Ranked first in the ROAD++ Challenge 2024

Enhanced recognition accuracy through multi-branch design

Abstract

This report presents our team's technical solution for participating in Track 3 of the 2024 ECCV ROAD++ Challenge. The task of Track 3 is atomic activity recognition, which aims to identify 64 types of atomic activities in road scenes based on video content. Our approach primarily addresses the challenges of small objects, discriminating between single object and a group of objects, as well as model overfitting in this task. Firstly, we construct a multi-branch activity recognition framework that not only separates different object categories but also the tasks of single object and object group recognition, thereby enhancing recognition accuracy. Subsequently, we develop various model ensembling strategies, including integrations of multiple frame sampling sequences, different frame sampling sequence lengths, multiple training epochs, and different backbone networks. Furthermore, we propose an atomic activity recognition data augmentation method, which greatly expands the sample space by flipping video frames and road topology, effectively mitigating model overfitting. Our methods rank first in the test set of Track 3 for the ROAD++ Challenge 2024, and achieve 69% mAP.