Hybrid classifier based human activity recognition using the silhouette and cells

• A new approach for the recognition of human activity using silhouette is proposed.
• The effectiveness of the proposed approach is measured using various classifiers.
• A new hybrid classification model is proposed to boost the recognition accuracy.
• The minimum classification error is achieved through a hybrid classification model.

The aim of this paper is to present a new approach for human activity recognition in a video sequence by exploiting the key poses of the human silhouettes, and constructing a new classification model. The spatio-temporal shape variations of the human silhouettes are represented by dividing the key poses of the silhouettes into a fixed number of grids and cells, which leads to a noise free depiction. The computation of parameters of grids and cells leads to modeling of feature vectors. This computation of parameters of grids and cells is further arranged in such a manner so as to preserve the time sequence of the silhouettes. To classify, these feature vectors, a hybrid classification model is proposed based upon the comparative study of Linear Discriminant Analysis (LDA), K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifier. The proposed hybrid classification model is a combination of SVM and 1-NN model and termed as ‘SVM–NN’. The effectiveness of the proposed approach of activity representation and classification model is tested over three public data sets i.e. Weizmann, KTH, and Ballet Movement. The comparative analysis shows that the proposed method is superior in terms of recognition accuracy to similar state-of-the-art methods.