Preprint Hypothesis Version 1 Preserved in Portico This version is not peer-reviewed

Optimised ARG Based Group Activity Recognition for Video Understanding

Pranjal Kumar * ORCID logo
Version 1 : Received: 10 June 2021 / Approved: 11 June 2021 / Online: 11 June 2021 (10:37:38 CEST)

How to cite: Kumar, P. Optimised ARG Based Group Activity Recognition for Video Understanding. Preprints 2021, 2021060313 (doi: 10.20944/preprints202106.0313.v1). Kumar, P. Optimised ARG Based Group Activity Recognition for Video Understanding. Preprints 2021, 2021060313 (doi: 10.20944/preprints202106.0313.v1).

Abstract

In this paper, we propose a robust video understanding model for activity recognition by learning the actor’s pair-wise correlations and relational reasoning, exploiting spatial and temporal information. In order to measure the similarity between the pair appearances and construct an actor relations map, the Zero Mean Normalized Cross-Correlation (ZNCC) and the Zero Mean Sum of Absolute Differences(ZSAD) is proposed to allow the Graph Convolution Network (GCN) to learn how to distinguish group actions. We recommend that MNASNet be used as the backbone to retrieve features. Experiments show a 38.50% and 23.7% reduction in training time in the 2-stage training process along with a 1.52% improvement in accuracy against traditional methods.

Subject Areas

group activity recognition; graph convolution network; video understanding; video analytics; activity recognition

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