# Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1608.06338 · HTML: https://mlsanity.com/paper/1608.06338/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 1 of 1)

- [Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition](https://mlsanity.com/paper/2102.05348/) (2021)

## Abstract (arXiv)

This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a ConvNet for recognition. The IDMM effectively encodes both spatial and temporal information and allows the fine-tuning with existing ConvNet models for classification without introducing millions of parameters to learn. The proposed method is evaluated on the Large-scale Continuous Gesture Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved the performance of 0.2655 (Mean Jaccard Index) and ranked 3rd place in this challenge.