Optimal Choice of Motion Estimation Methods for Fine-Grained Action Classification with 3D Convolutional Networks
Abstract
Detecting and classifying human actions in videos is oneof the current challenges in visual content analysis and mining. This paper presents a method for performing a fine-grained classification of sport actions using a Siamese Spatio-Temporal Convolutional Neural Network (SSTCNN) model.This model takes RGB images and Optical Flow field as in-put data. Our first contribution is the comparison of different Optical flow methods and a study of their influence on the classification score. We also present different normalization methods for the optical flow that drastically impact results,boosting performances from 44% to 74% of accuracy. Our second contribution is the detection and classification of actions in videos performed using a sliding temporal window.It leads to a satisfying score of 81.3% over the whole dataset TTStroke-21.
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