Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2020

Abstract

The task of person-level action recognition in complex events aims to densely detect pedestrians and individually predict their actions from surveillance videos. In this paper, we present a simple yet efficient pipeline for this task, referred to as TSD-TSM networks. Firstly, we adopt the TSD detector for the pedestrian localization on each single keyframe. Secondly, we generate the sequential ROIs for a person proposal by replicating the adjusted bounding box coordinates around the keyframe. Particularly, we propose to conduct straddling expansion and region squaring on the original bounding box of a person proposal to widen the potential space of motion and interaction and lead to a square box for ROI detection. Finally, we adapt the TSM classifier on the generated ROI sequences to perform action classification and further adopt late fusion to promote the prediction. Our proposed pipeline achieved the 3rd place in the ACM-MM 2020 grand challenge, i.e., Large-scale Human-centric Video Analysis in Complex Events (Track-4), obtaining final 15.31% wf-mAP@avg and 20.63% f-mAP@avg on the testing set.

Keywords

complex events, human action recognition, pedestrian detection

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 28th ACM International Conference on Multimedia, MM 2020, Seattle, October 12–16

First Page

4699

Last Page

4702

ISBN

9781450379885

Identifier

10.1145/3394171.3416276

Publisher

Association for Computing Machinery, Inc

City or Country

Virtual Conference

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