# Joint Hand Detection and Rotation Estimation by Using CNN

arXiv: https://arxiv.org/abs/1612.02742 · HTML: https://mlsanity.com/paper/1612.02742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild](https://mlsanity.com/paper/2409.12259/) (2024)
- [Serial-parallel Multi-Scale Feature Fusion for Anatomy-Oriented Hand Joint Detection](https://mlsanity.com/paper/2102.09757/) (2021)
- [InterTracker: Discovering and Tracking General Objects Interacting with Hands in the Wild](https://mlsanity.com/paper/2308.03061/) (2023)

## Abstract (arXiv)

Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other.