Recognition of Activities of Daily Living for Smart Home Environments

The recognition of Activities of Daily Living (ADL) from video can prove particularly useful in assisted living and smart home environments, as behavioral and lifestyle profiles can be constructed through the recognition of ADLs over time. Often, existing methods for recognition of ADLs have a very high computational cost, which makes them unsuitable for real time or near real time applications. In this work we present a novel method for recognizing ADLs with accuracy comparable to the state of the art, at a lowered computational cost. Comprehensive testing of the best existing descriptors, encoding methods and BoW/SVM based classification methods takes place to determine the optimal recognition solution. A statistical method for determining the temporal duration of extracted trajectories is also introduced, to streamline the recognition process and make it less ad-hoc. Experiments take place with benchmark ADL datasets and a newly introduced set of ADL recordings of elderly people with dementia as well as healthy individuals. Our algorithm leads to accurate recognition rates, comparable or better than the State of the Art, at a lower computational cost.

[1]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Karsten P. Ulland,et al.  Vii. References , 2022 .

[3]  Mubarak Shah,et al.  A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.

[4]  Cordelia Schmid,et al.  Actions in context , 2009, CVPR.

[5]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[6]  Jiebo Luo,et al.  Recognizing realistic actions from videos “in the wild” , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  G. Moustakides.  Optimal stopping times for detecting changes in distributions , 1986 .

[8]  Andrew Zisserman,et al.  The devil is in the details: an evaluation of recent feature encoding methods , 2011, BMVC.

[9]  Christopher Joseph Pal,et al.  Activity recognition using the velocity histories of tracked keypoints , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[10]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  B. Achiriloaie,et al.  VI REFERENCES , 1961 .

[12]  Yiannis Kompatsiaris,et al.  Robust Temporal Activity Templates Using Higher Order Statistics , 2009, IEEE Transactions on Image Processing.

[13]  E. S. Page.  CONTINUOUS INSPECTION SCHEMES , 1954 .

[14]  Ronen Basri,et al.  Actions as space-time shapes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[15]  Cordelia Schmid,et al.  A Spatio-Temporal Descriptor Based on 3D-Gradients , 2008, BMVC.

[16]  Cordelia Schmid,et al.  Aggregating Local Image Descriptors into Compact Codes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Cordelia Schmid,et al.  Actions in context , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Eric Moreau,et al.  Unbiased Efficient Estimator of the Fourth-Order Cumulant for Random Zero-Mean Non-i.i.d. Signals: Particular Case of MA Stochastic Process , 2010, IEEE Transactions on Information Theory.

[19]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Cordelia Schmid,et al.  Action recognition by dense trajectories , 2011, CVPR 2011.

[21]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .