Name
Abstract | ||
|---|---|---|
The performance of deep neural networks is strongly influenced by the quantity and quality of annotated data. Most of the large activity recognition datasets consist of data sourced from the web, which does not reflect challenges that exist in activities of daily living. In this paper, we introduce a large real-world video dataset for activities of daily living: Toyota Smarthome. The dataset consists of 16K RGB+D clips of 31 activity classes, performed by seniors in a smarthome. Unlike previous datasets, videos were fully unscripted. As a result, the dataset poses several challenges: high intra-class variation, high class imbalance, simple and composite activities, and activities with similar motion and variable duration. Activities were annotated with both coarse and fine-grained labels. These characteristics differentiate Toyota Smarthome from other datasets for activity recognition. As recent activity recognition approaches fail to address the challenges posed by Toyota Smarthome, we present a novel activity recognition method with attention mechanism. We propose a pose driven spatio-temporal attention mechanism through 3D ConvNets. We show that our novel method outperforms state-of-the-art methods on benchmark datasets, as well as on the Toyota Smarthome dataset. We release the dataset for research use. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCV.2019.00092 | 2019 IEEE/CVF International Conference on Computer Vision (ICCV) |
Keywords | Field | DocType |
activity recognition method,Toyota Smarthome dataset,real-world activities,daily living,deep neural networks,annotated data,activity recognition datasets,real-world video dataset,16K RGB+D clips | Activities of daily living,Activity recognition,Pattern recognition,Computer science,Artificial intelligence,RGB color model,Deep neural networks,Machine learning | Conference |
Volume | Issue | ISSN |
2019 | 1 | 1550-5499 |
ISBN | Citations | PageRank |
978-1-7281-4804-5 | 1 | 0.36 |
References | Authors | |
13 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srijan Das | 1 | 12 | 4.90 |
| Rui Dai | 2 | 49 | 14.71 |
| Michal Koperski | 3 | 33 | 6.04 |
| Luca Minciullo | 4 | 1 | 2.38 |
| Lorenzo Garattoni | 5 | 27 | 3.20 |
| Francois Bremond | 6 | 838 | 56.54 |
| Gianpiero Francesca | 7 | 91 | 11.26 |