Topological Features for Monitoring Human Activities at Distance

Abstract

In this paper, a topological approach for monitoring human activities is presented. This approach makes possible to protect the person’s privacy hiding details that are not essential for processing a security alarm. First, a stack of human silhouettes, extracted by background subtraction and thresholding, are glued through their gravity centers, forming a 3D digital binary image $I$. Secondly, different orders of the simplices are applied on a simplicial complex obtained from $I$, which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagrams according to each order. The measure cosine is used to give a similarity value between topological signatures. In this way, the powerful topological tool known as persistent homology is novelty adapted to deal with gender classification, person identification, carrying bag detection and simple action recognition. Four experiments show the strength of the topological feature used; three of they use the CASIA-B database, and the fourth use the KTH database to present the results in the case of simple actions recognition. In the first experiment the named topological signature is evaluated, obtaining $98.8\,\%$ (lateral view) of correct classification rates for gender identification. In the second one are shown results for person identification, obtaining an average of $98.5\,\%$. In the third one the result obtained is $93.8\,\%$ for carrying bag detection. And in the last experiment the results were $97.7\,\%$ walking and $97.5\,\%$ running, which were the actions took from the KTH database.