Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
This work addresses gesture recognition for human-computer interaction, but it is incremental as it builds on existing ConvNet methods with a specific improvement for depth data.
The paper tackles continuous gesture recognition from depth sequences by segmenting gestures based on quantity of movement and using an Improved Depth Motion Map with a convolutional neural network, achieving a Mean Jaccard Index of 0.2655 and ranking 3rd in the ChaLearn LAP 2016 challenge.
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 $3^{rd}$ place in this challenge.