26,147 research outputs found
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
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 place in
this challenge but was very close to the best performance even though we only
used depth data.Comment: arXiv admin note: text overlap with arXiv:1608.0633
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model
individual dynamics for single-person action recognition due to its ability of
modeling the temporal information in various ranges of dynamic contexts.
However, existing RNN models only focus on capturing the temporal dynamics of
the person-person interactions by naively combining the activity dynamics of
individuals or modeling them as a whole. This neglects the inter-related
dynamics of how person-person interactions change over time. To this end, we
propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to
model the long-term inter-related dynamics between two interacting people on
the bounding boxes covering people. Specifically, for each frame, two
sub-memory units store individual motion information, while a concurrent LSTM
unit selectively integrates and stores inter-related motion information between
interacting people from these two sub-memory units via a new co-memory cell.
Experimental results on the BIT and UT datasets show the superiority of
Co-LSTSM compared with the state-of-the-art methods
- …