Papers › Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
Pichao Wang, Wanqing Li, Song Liu, Zhimin Gao, Chang Tang, Philip Ogunbona
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based representations enable us to fine-tune the existing ConvNets models trained on image data for classification of depth sequences, without introducing large parameters to learn. Upon the proposed representations, a convolutional Neural networks (ConvNets) based method is developed for gesture recognition and evaluated on the Large-scale Isolated Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The method achieved 55.57\% classification accuracy and ranked 2ⁿᵈ place in this challenge but was very close to the best performance even though we only used depth data.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hand Gesture Recognition | ChaLearn val | Wang et al. | Accuracy | 39.23 | #2 of 3 | Archive leaderboard | report |
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