Search

Activity interpretation in videos results not only in recognition or labeling of dominant activities, but also in semantic descriptions of scenes. Towards this broader goal, we present a combinatorial approach that assumes availability of algorithms for detecting and labeling objects and basic actions in videos, albeit with some errors. Given these uncertain labels and detected objects, we link them into interpretable structures using the domain knowledge, under the framework of Grenander's general pattern theory. Here a semantic description is built using basic units, termed generators, that represent either objects or actions. These generators have multiple out-bonds, each associated with different types of domain semantics, spatial constraints, and image evidence. The generators combine, according to a set of pre-defined combination rules that capture domain semantics, to form larger configurations that represent video interpretations. This framework derives its representational power from flexibility in size and structure of configurations. We impose a probability distribution on the configuration space, with inferences generated using a Markov chain Monte Carlo-based simulated annealing process. The primary advantage of the approach is that it handles known challenges-appearance variabilities, errors in object labels, object clutter, simultaneous events, etc-without the need for exponentially-large (labeled) training data. Experimental results demonstrate its ability to successfully provide interpretations under clutter and the simultaneity of events. They show: (1) a performance increase of more than 30 % over other state-of-the-art approaches using more than 5000 video units from the Breakfast Actions dataset, and (2) an overall recall and precision improvement of more than 50 and 100 %, respectively, on the YouCook data set. [ABSTRACT FROM AUTHOR]

We develop a combinatorial approach to represent and infer semantic interpretations of video contents using tools from Grenander’s pattern theory. Semantic structures for video interpretation are formed using generators and bonds, the fundamental units of representation in pattern theory. Generators represent features and ontological items, such as actions and objects, whereas bonds are threads used to connect generators while respecting appropriate constraints. The resulting configurations of partially-connected generators are termed scene interpretations. Our goal is to parse a given video data set into high-probability configurations. The probabilistic models are imposed using energies that have contributions from both data (classification scores) and prior information (ontological constraints, co-occurrence frequencies, etc). The search for optimal configurations is based on an MCMC, simulated-annealing algorithm that uses simple moves to propose configuration changes and to accept/reject them according to the posterior energy. In contrast to current graphical methods, this framework does not preselect a neighborhood structure but tries to infer it from the data. The proposed framework is able to obtain 20% higher classification rates, compared to a purely machine learning-based baseline, despite artificial insertion of low-level processing errors. In an uncontrolled scenario, video interpretation performance rates are found to be double that of the baseline. [ABSTRACT FROM AUTHOR]