Human activities recognition based on poisson equation evaluation and bidirectional 2DPCA

A novel algorithm for the human activities recognition based on the Poisson images and via bidirectional two-dimensional principal component analysis (2DPCA) is presented in this note, where the Poisson images are defined by solving the Poisson equations to re-interpret the motion accumulation image (MAI). More precisely, firstly, object detection based on the Gaussian Mixture Model (GMM) is applied to acquire the binary images including moving human blobs; secondly, the Poisson image is defined to make the features extracted in the sequel robust to possible incomplete human blobs; thirdly, the principal component analysis (PCA), 2DPCA and bidirectional 2DPCA are applied, respectively, to extract the feature vectors; and finally, the nearest neighbour (NN) classifier is used to recognize the human activities. Simulation results on Weizmann database confirm the recognition performance of the proposed algorithm. Comparisons in terms of classification accuracy and time consumption in between the three methods show that the bidirectional 2DPCA is optimal.