Exploiting the features of deep residual network with SVM classifier for human posture recognition

Over the last decade, there have been a lot of advances in the area of human posture recognition. Among multiple approaches proposed to solve this problem, those based on deep learning have shown promising results. Taking another step in this direction, this paper analyzes the performance of deep learning-based hybrid architecture for fall detection, In this regard, the fusion of the residual network (ResNet-50) deep features with support vector machine (SVM) at the classification layer has been considered. The proposed approach outperforms the existing methods yielding an accuracy of 98.82%, 97.95%, and 99.98% on three datasets i.e. Multi-Camera Fall (MCF) using four postures, UR Fall detection (URFD) using four postures, and UP-Fall detection (UPFD) using four postures respectively. It is important to mention that the existing methods achieve accuracies of 97.9%, 97.33%, and 95.64% on the MCF, URDF and UPFD datasets, respectively. Moreover, we achieved 100% accuracy on the UPFD two-posture task. The URFD and MCF datasets have been utilized to assess the fall detection performance of our method under a realistic environment (e.g. camouflage, occlusion, and variation in lighting conditions due to day/night lighting variation). For comparison purposes, we have also performed experiments using six state-of-the-art deep learning networks, namely; ResNet-50, ResNet-101, VGG-19, InceptionV3, MobileNet, and Xception. The results demonstrate that the proposed approach outperforms other network models both in terms of accuracy and time efficiency. We also compared the performance of SVM with Naive Bayes, Decision Tree, Random Forest, KNN, AdaBoost, and MLP used at the classifier layer and found that SVM outperforms or is on par with other classifiers.