Human activity recognition from UAV videos using a novel DMLC-CNN model

Abstract

Human Activity Recognition (HAR) remains a challenging issue that requires to be resolved. Utilizing images, smart phones, or sensors, HAR could be done. Recent studies have explored HAR utilizing Unmanned Aerial Vehicle (UAV) videos. But, owing to numerous restrictions correlated to the platform, HAR from videos captured by drones remains a challenge. To resolve this issue, a novel mechanism for HAR from UAV videos utilizing the Diminutive Multi-Dimensional Locality Coding based Convolutional Neural Network (DMLC-CNN) model is proposed. Primarily, the input video is converted into frames; also, Bounding Boxes (BBs) are produced for humans. For attaining the number of clusters grounded on human presence, the BB is given to the Robust Lucrative Sensitive Amalgam K-Nearest Neighbor (RoLSA-KNN) clustering. After that, utilizing the LR-fit-centric Superior Photogrammetric Silhouette (LR-SPS) segmentation, the humans' silhouettes are segmented. The attained output is divided into patches; then, it is converted into stacked layer frames. Afterward, these frames are converted into a 3D kernel. The Diminutive Multi-Dimensional Hyper Graph (DMHG) extracted the features; then, the Locality-constrained Orthonormal Feature Space Coding (LOFSC) Algorithm encoded the extracted features. Lastly, for recognizing Human Activity (HA), DMLC-CNN is wielded. The proposed framework is contrasted with the conventional techniques. After experimental evaluation, the proposed mechanism was found to be more efficient in HAR.