Fast and Robust Hand Tracking Using Detection-Guided Optimization (1602.04124v1)

Abstract: Markerless tracking of hands and fingers is a promising enabler for human-computer interaction. However, adoption has been limited because of tracking inaccuracies, incomplete coverage of motions, low framerate, complex camera setups, and high computational requirements. In this paper, we present a fast method for accurately tracking rapid and complex articulations of the hand using a single depth camera. Our algorithm uses a novel detection-guided optimization strategy that increases the robustness and speed of pose estimation. In the detection step, a randomized decision forest classifies pixels into parts of the hand. In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth. Our approach needs comparably less computational resources which makes it extremely fast (50 fps without GPU support). The approach also supports varying static, or moving, camera-to-scene arrangements. We show the benefits of our method by evaluating on public datasets and comparing against previous work.

• The paper introduces a unified detection-guided optimization framework that combines generative tracking with discriminative detection for robust, real-time hand pose estimation.
• It employs a Gaussian mixture model for smooth, analytic optimization and integrates a detection-augmented energy function to recover from local tracking errors.
• The method achieves state-of-the-art performance at over 50 FPS using a single depth camera, demonstrating its efficiency and accuracy in dynamic hand tracking.

Fast and Robust Hand Tracking Using Detection-Guided Optimization

The paper "Fast and Robust Hand Tracking Using Detection-Guided Optimization," presented at CVPR 2015, introduces an innovative method for hand pose estimation that tackles the persistent challenges of real-time and accurate hand tracking. This paper, conducted by researchers from the Max Planck Institute for Informatics, Saarland University, and Aalto University, focuses on overcoming several limitations associated with markerless hand tracking, particularly in the context of computational efficiency and robustness.

Key Contributions

The authors propose a novel detection-guided optimization strategy that facilitates efficient and reliable hand tracking using a single depth camera. This method is distinguished by several key contributions:

1. Detection-Guided Optimization Strategy: The integration of model-based generative tracking with discriminative pose detection paves the way for a unified framework that enhances robustness and computational speed. The strategy leverages a combination of these two traditionally distinct approaches, thus optimizing the tracking process while minimizing typical failures that occur when they are used independently.
2. Gaussian Mixture Model Representation: The paper introduces a Gaussian mixture model representation for both the depth data and hand model, differing from the traditional use of geometric primitives like cylinders and spheres. This representation supports efficient, analytically smooth optimization processes, enabling the derivation of analytic gradients, which significantly accelerates pose estimation.
3. Detection-Augmented Energy Function: By employing a randomized decision forest to classify pixels into parts of the hand, the method incorporates discriminative evidence into the optimization process. This approach helps in recovering from erroneous local pose optimizations, a common drawback in pure generative methods.
4. Late Fusion Particle Optimization: The authors incorporate a multi-hypothesis optimization strategy using particles, combining computationally efficient local optimization with multiple hypothesis generation. This late fusion approach enhances the ability to recover from lost tracks and improves the robustness of tracking rapid hand motions.

Results and Evaluation

The method achieves impressive results, boasting state-of-the-art accuracy in tracking hand movements, even under rapid and complex articulations. The proposed method achieves frame rates of over 50 FPS without the necessity for GPU acceleration, a notable achievement underscoring the algorithm's efficiency. Evaluations against public datasets highlight the efficacy and robustness of the approach when compared to existing methods, demonstrating lower average tracking errors and higher stability across various scenarios.

Implications and Future Directions

The practical implications of this research are significant for human-computer interaction systems, particularly in applications requiring real-time response from hand-tracking systems, such as in augmented reality and smart device interfaces. The method's capacity to function accurately without a multi-camera setup or GPU support broadens its potential applicability and accessibility.

Theoretically, the paper sets a precedent in integrating discriminative and generative methods within a coherent optimization framework, potentially inspiring further exploration into hybrid optimization strategies within computer vision tasks. Future developments could extend this approach to handle interactions involving multiple hands or in environments involving hand-object interactions, which remain challenging areas in the current scope of computer vision research.

This paper contributes a significant advancement in the field of real-time hand tracking, demonstrating a compelling approach that balances efficiency, robustness, and accuracy, with potential implications extending into various interactive technology domains.