IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition (2005.02134v2)

Abstract: In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4,000 gesture samples and 800,000 RGB frames from 50 distinct subjects. We design 13 different static and dynamic gestures focused on interaction with touchless screens. We especially consider the scenario when continuous gestures are performed without transition states, and when subjects perform natural movements with their hands as non-gesture actions. Gestures were collected from about 30 diverse scenes, with real-world variation in background and illumination. With our dataset, the performance of three 3D-CNN models is evaluated on the tasks of isolated and continuous real-time HGR. Furthermore, we analyze the possibility of increasing the recognition accuracy by adding multiple modalities derived from RGB frames, i.e., optical flow and semantic segmentation, while keeping the real-time performance of the 3D-CNN model. Our empirical study also provides a comparison with the publicly available nvGesture (NVIDIA) dataset. The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR. Our dataset and pre-trained models used in the evaluation are publicly available at https://github.com/GibranBenitez/IPN-hand.

Overview of "IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition"

The paper presents a novel video dataset named IPN Hand, which is created and proposed as a benchmark for continuous hand gesture recognition (HGR) systems. Recognizing hand gestures is a critical aspect of human-computer interaction and has numerous applications, such as in automotive and consumer electronics. Despite the advancements in deep learning techniques for this purpose, existing publicly available datasets often do not encompass sufficient real-world elements necessary for deploying robust and efficient HGR models. This research addresses this gap by providing a comprehensive dataset designed for training and evaluating sophisticated neural networks under realistic conditions.

Dataset Characteristics

IPN Hand aggregates over 4,000 gesture samples and 800,000 frames from 50 subjects, ensuring a rich diversity of gesture data. It is noteworthy for its inclusion of 13 distinct static and dynamic gestures that are pertinent for interactions with touchless screens. Notably, it considers the execution of continuous gestures without transitional states and incorporates natural hand movements as non-gesture actions, captured across approximately 30 different settings featuring real-world variations like cluttered backgrounds and inconsistent lighting. Such design ensures the dataset's fitness for addressing key challenges in real-world continuous gesture recognition scenarios.

Experimental Evaluation and Methodology

The dataset was evaluated using three state-of-the-art 3D Convolutional Neural Network (3D-CNN) models on both isolated and continuous gesture recognition tasks. The paper investigates enhancements in recognition accuracy through the integration of multiple modalities derived from RGB frames, specifically optical flow and semantic segmentation, while maintaining a focus on real-time performance. The comparative analysis with the nvGesture dataset revealed compelling insights. The ResNext-101 model exhibited a 30% accuracy drop when tested on the IPN Hand dataset, accentuating the latter’s complexity and capacity to serve as an adequate benchmark.

Implications and Future Developments

The introduction of the IPN Hand dataset holds significant implications for the field of continuous HGR. It sets a new standard by addressing the limitations of existing datasets, notably the scarcity of real-world scenario inclusivity. This enables the development and evaluation of more generalized and robust models capable of performing reliably in natural contexts.

From a theoretical standpoint, the research posits the advantage of multi-modality approaches in refining gesture recognition systems. The utilization of semantic segmentation as a lightweight alternative to optical flow presents a promising direction for optimizing real-time performance without compromising accuracy significantly. This pivot to efficient multi-modal methods aligns with increasing demands for high-performance, low-computational cost solutions in AI applications.

Looking to the future, the IPN Hand dataset and accompanying methodological insights may stimulate further research into exploring deeper integration of various modalities, or even new ones, for enhanced real-time gesture recognition. Additionally, its application scope is likely to expand beyond touchless screens to encompass broader interfaces within the Internet of Things (IoT) ecosystem, augmented reality (AR), and virtual reality (VR) environments, propelling forward innovations in interactive technologies.