Fall Detection Using the Histogram of Oriented Gradients and Decision-Based Fusion

As the number of fall incidents among elderly people and patients are continuously growing, researches boosted their researches to propose efficient automatic fall detection systems. In particular, they formulated the fall detection problem as a supervised learning task where some visual features are extracted from the video frames and used to automatically identify the position of a human as “Fall” or “Non-Fall” based on a model learned using labeled training frames. Despite the promising reported results, existing fall detection systems exhibit noticeable room for improvement. Learner fusion which builds multiple models and aggregates their respective decisions is an alternative that would improve the fall detection performance. In this paper, an image-based fall detection system that captures the visual property and the spatial position of the human body using the Histogram of Oriented Gradient from the video frames is proposed. Then, the extracted features are used to train three classification models. Namely, the Naïve Bayes, the K-Nearest Neighbors and the Support Vector Machine algorithms are adopted. Next, the majority vote is used to aggregate the decisions of the individual learners. The proposed system was assessed using a standard dataset and yielded promising results. Standard performance measures along with the statistical significance t-test were used to prove that the fall detection system based on majority vote fusion outperforms the individual classifier based approaches.