One-Shot-Learning Gesture Recognition using HOG-HOF Features
This work addresses gesture recognition with limited training data, which is an incremental improvement for computer vision applications.
The paper tackles one-shot-learning gesture recognition using HOG-HOF features on the ChaLearn Gesture Dataset, achieving results that outperform other published methods and narrow the gap between human and algorithm performance.
The purpose of this paper is to describe one-shot-learning gesture recognition systems developed on the \textit{ChaLearn Gesture Dataset}. We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (Histogram of Optical Flow) for parallel temporal segmentation and recognition. The Quadratic-Chi distance family is used to measure differences between histograms to capture cross-bin relationships. We also propose a new algorithm for trimming videos --- to remove all the unimportant frames from videos. We present two methods that use combination of HOG-HOF descriptors together with variants of Dynamic Time Warping technique. Both methods outperform other published methods and help narrow down the gap between human performance and algorithms on this task. The code has been made publicly available in the MLOSS repository.