The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust temporal activity templates using higher order statistics
IEEE Transactions on Image Processing
Modeling temporal structure of decomposable motion segments for activity classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
IEEE Transactions on Information Theory
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Aggregating Local Image Descriptors into Compact Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human focused action localization in video
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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Activity recognition is one of the most active topics within computer vision. Despite its popularity, its application in real life scenarios is limited because many methods are not entirely automated and consume high computational resources for inferring information. In this work, we contribute two novel algorithms: (a) one for automatic video sequence segmentation - elsewhere referred to as activity spotting or activity detection - and (b) a second one for reducing activity representation computational cost. Two Bag-of-Words (BoW) representation schemas were tested for recognition purposes. A set of experiments was performed, both on publicly available datasets of activities of daily living (ADL), but also on our own ADL dataset with both healthy subjects and people with dementia, in realistic, life-like environments that are more challenging than those of benchmark datasets. Our method is shown to provide results better than, or comparable with, the SoA, while we also contribute a realistic ADL dataset to the community.