Recognizing human actions using multiple features

In this paper, we propose a framework that fuses multiple features for improved action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use two types of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (or cuboids), and ii) a quantized vocabulary of spin-images, which aims to capture the shape deformation of the actor by considering actions as 3D objects (x, y, t). To optimally combine these features, we treat different features as nodes in a graph, where weighted edges between the nodes represent the strength of the relationship between entities. The graph is then embedded into a k-dimensional space subject to the criteria that similar nodes have Euclidian coordinates which are closer to each other. This is achieved by converti...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Jingen Liu, Saad Ali, Mubarak Shah
• Keywords: Computer Vision, CVPR 2008, Graph Laplacian Matrix, Improved Action Recognition, Multiple Features, Quantized Vocabulary, Similar Nodes