Structured prediction with short/long-range dependencies for human activity recognition from depth skeleton data | IEEE Conference Publication | IEEE Xplore

Structured prediction with short/long-range dependencies for human activity recognition from depth skeleton data


Abstract:

One of the main abilities that the robots need to maintain is to efficiently communicate with people in a humanly manner. Thus, human activity recognition (HAR) would be ...Show More

Abstract:

One of the main abilities that the robots need to maintain is to efficiently communicate with people in a humanly manner. Thus, human activity recognition (HAR) would be an integral part of such a human-robot interaction system. One of the major challenges in HAR is that the individuals perform their activities in different manners. Furthermore, there is a very wide range of different types of activities that the robots would require to understand. Some activities are simple, quick and short (e.g., sit down), while many others are complex, have many details and span through a long range of time (e.g., wearing contact lens). In this paper, we model the recognition of activities into a sequence-labeling problem and propose a new probabilistic graphical model (PGM) that can recognize both short/long-range activities, by introducing a hierarchical classification model and including extra links and loopy conditions in our PGM. To optimize the PGM and obtain its parameters during training, we use a structured prediction technique, a general framework that involves latent structured support vector machines (LSSVM) and hidden-state conditional random fields (HCRF). We evaluate our method on two widely used datasets (CAD-60 & UT-Kinect) that contain both activity types. Our obtained results are promising and show that our method can recognize both types of activities effectively, while most of the previous works only focused on one of these two major types. We further explore distributed processing techniques, since our method can easily be distributed over processing nodes. We also propose an efficient divide-and-merge technique to further speedup the training step.
Date of Conference: 24-28 September 2017
Date Added to IEEE Xplore: 14 December 2017
ISBN Information:
Electronic ISSN: 2153-0866
Conference Location: Vancouver, BC, Canada

References

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