A survey of moment-based techniques for unoccluded object representation and recognition
CVGIP: Graphical Models and Image Processing
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Searching for Complex Human Activities with No Visual Examples
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram of oriented rectangles: A new pose descriptor for human action recognition
Image and Vision Computing
Human Action Recognition Using Optical Flow Accumulated Local Histograms
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
A survey on vision-based human action recognition
Image and Vision Computing
Dynamic textures for human movement recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
Human Action Recognition by Negative Space Analysis
CW '10 Proceedings of the 2010 International Conference on Cyberworlds
A novel human motion recognition method based on eigenspace
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Fast action recognition using negative space features
Expert Systems with Applications: An International Journal
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A region based technique is proposed here to recognize human actions where features are extracted from the surrounding regions of a human silhouette termed as negative space. Negative space has the ability to describe poses as good as the positive spaces (i.e. silhouette based methods) with the advantage of describing poses by simple shapes. Moreover, it can be combined with silhouette based methods to make an improved system in terms of accuracy and computational costs. Main contributions in this paper are two folded: proposed a method to isolate and discard long shadows from segmented binary images, and generalize the idea of negative space to work under viewpoint changes. The system consists of hierarchical processing of background segmentation, shadow elimination, speed calculation, region partitioning, shape based feature extraction and sequence matching by Dynamic Time Warping. The recognition accuracy of our system for Weizmann dataset is 100% and for KTH dataset is 95.49% which are comparable with state-of-the-art methods.