Motion-Based Representations For Activity Recognition

Abstract

This work addresses the activity recognition problem. We propose two different representations based on motion information for activity recognition. The first representation is a novel temporal stream for two-stream Convolutional Neural Networks (CNNs) that receives as input images computed from the optical flow magnitude and orientation to learn the motion in a better and richer manner. The method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. The second representation is a novel skeleton image representation to be used as input of CNNs. The approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Experiments carried out on challenging well-known activity recognition datasets (UCF101, NTU RGB+D 60 and NTU RGB+D 120) demonstrate that the proposed representations achieve results in the state of the art, indicating the suitability of our approaches as video representations.

Simple Approach for Violence Detection in Real-Time Videos Using Pose Estimation With Azimuthal Displacement and Centroid Distance as Features

“…The last layer in this architecture is a fully connected one, which performs the final classification putting the LSTM output into the two final classes, namely violence and nonviolence (Caetano, 2020;Peixoto et al, 2019).…”