Abstract
This paper explores the possibility of using low-level activity spotting for daily routine recognition. Using occurrence statistics of low-level activities and simple classifiers based on their statistics allows to train a discriminative classifier for daily routine activities such as working and commuting. Using a recently published data set we find that the number of required low-level activities is surprisingly low, thus, enabling efficient algorithms for daily routine recognition through low-level activity spotting. More specifically we employ the JointBoosting-framework using low-level activity spotters as weak classiers. By using certain low-level activities as support, we achieve an overall recall rate of over 90% and precision rate of over 88%. Tuning down the weak classifiers using only 2.61% of the original data still yields recall and precision rates of 80% and 83%.
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References
Clarkson, B., Pentland, A.: Unsupervised clustering of ambulatory audio and video. In: ICASSP (1999)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Statist. 28(2), 337–407 (2000)
Huynh, T., Blanke, U., Schiele, B.: Scalable recognition of daily activities with wearable sensors. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 50–67. Springer, Heidelberg (2007)
Huynh, T., Fritz, M., Schiele, B.: Discovery of activity patterns using topic models. In: Proceedings of the 10th International Conference on Ubiquitous Computing (Ubicomp) (2008)
Lester, J., Choudhury, T., Kern, N., Borriello, G., Hannaford, B.: A hybrid discriminative/generative approach for modeling human activities. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (2005)
Minnen, D., Starner, T., Essa, I., Isbell, C.: Discovering characteristic actions from on-body sensor data. In: Proceedings of the 10th IEEE International Symposium on Wearable Computers (ISWC) (2006)
Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Proceedings of the 4th IEEE International Conference on Multimodal Interfaces (2002)
Torralba, A., Murphy, K., Freeman, W.: Sharing visual features for multiclass and multiview object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2004)
Van Laerhoven, K., Kilian, D., Schiele, B.: Using rhythm awareness in long-term activity recognition. In: Proceedings of the 12th IEEE International Symposium on Wearable Computers (ISWC) (2008)
Viola, P., Viola, P., Jones, M., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition (CVPR) (2001)
Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I., Lathoud, G.: Modelling individual and group actions in meetings: a two-layer hmm framework. In: Computer Vision and Pattern Recognition Workshop (2004)
Zinnen, A., Schiele, B.: A new Approach to Enable Gesture Recognition in Continuous Data Streams. In: Proceedings of the 12th IEEE International Symposium on Wearable Computers (2008)
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© 2009 Springer-Verlag Berlin Heidelberg
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Blanke, U., Schiele, B. (2009). Daily Routine Recognition through Activity Spotting. In: Choudhury, T., Quigley, A., Strang, T., Suginuma, K. (eds) Location and Context Awareness. LoCA 2009. Lecture Notes in Computer Science, vol 5561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01721-6_12
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DOI: https://doi.org/10.1007/978-3-642-01721-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01720-9
Online ISBN: 978-3-642-01721-6
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