Classification of Human Whole-Body Motion using Hidden Markov Models
It addresses the cumbersome and error-prone process of annotating human motions in databases, though it appears incremental as it builds on existing HMM methods.
This bachelor's thesis tackles the multi-label classification problem for human whole-body motion by using Hidden Markov Models (HMMs), achieving accuracies of 98.02% and 93.39% on a test set of 454 motions with two different approaches.
Human motion plays an important role in many fields. Large databases exist that store and make available recordings of human motions. However, annotating each motion with multiple labels is a cumbersome and error-prone process. This bachelor's thesis presents different approaches to solve the multi-label classification problem using Hidden Markov Models (HMMs). First, different features that can be directly obtained from the raw data are introduced. Next, additional features are derived to improve classification performance. These features are then used to perform the multi-label classification using two different approaches. The first approach simply transforms the multi-label problem into a multi-class problem. The second, novel approach solves the same problem without the need to construct a transformation by predicting the labels directly from the likelihood scores. The second approach scales linearly with the number of labels whereas the first approach is subject to combinatorial explosion. All aspects of the classification process are evaluated on a data set that consists of 454 motions. System 1 achieves an accuracy of 98.02% and system 2 an accuracy of 93.39% on the test set.