Abstract
This paper deals with the classification task of human activities in videos with learning using different image sequences as input methods. Image sequences of purely binary silhouette (or shape), that is Raw Silhouette Representation (RSR), distance transform (DT) images of RSR, edge images of RSR, Silhouette History Image (SHI) and Silhouette Energy Image (SEI), image sequences of Wavelet Transform (WT) are used for training and testing using spectral regression discriminant analysis. Hausdorff distance was used for similarity measures to match the embedded action trajectories. Then action classification is achieved in a K-nearest neighbour framework. Using these different input methods we achieved 100% for all the cases except WT cases. From the results, it is evident that SHI and SEI are effective input method in terms of time and space consumption.
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