DEEP LEARNED FEATURE TECHNIQUE FOR HUMAN ACTION RECOGNITION IN THE MILITARY USING NEURAL NETWORK CLASSIFIER - Kolawole, Adeola O; Irhebhude, Martins E; Odion, Philip O (Science Journal of University of Zakho, 2025)
Assessing military trainee in an obstacle crossing competition requires an instructor to go along with participants or be strategically placed. These assessments sometimes suffer from fatigue or biasedness on the part of instructors. There is the need to have a system that can easily recognize various human actions involved in obstacle crossing and also give a fair assessment of the whole process. In this paper, VGG16 model features with neural network classifier is used to recognize human actions in a military obstacle-crossing competition video sequence involving multiple participants performing different activities. The dataset used was captured locally during military trainees’ obstacle-crossing exercises at a military training institution to achieve the objective. Images were segmented into background and foreground using a Grabcut-based segmentation algorithm. On the foreground masked images, features were extracted and used for classification with neural network. This method used the VGG16 model to automatically extract deep learned features at the max-pooling layer and the input presented to neural network classifier for classification into the various classes of human actions achieving 90% recognition accuracy which is at training time of 104.91secs. The accuracy obtained showed 3.6% performance improvement when compared to selected state-of-the-art model. The model also achieved 90.1% precision value and recall of 90.2%. Although many studies have focused on human recognition action recognition in several application areas, this study introduced a novel model for real time recognition of fifteen different classes of complex actions involving multiple participants during obstacle crossing competition in a military environment leveraging on the strength of deep learning and neural network classifier. This study will be of immense unbiased benefit to the military in the assessment of a trainee’s performance during training exercises or competitions.
Keywords: VGG16; Neural Network Classifier; Obstacle Crossing Exercise; Military Training; deep learning