While recognizing some kinds of human motion patterns requires detailed feature representation and tracking, many of them can be recognized using global features. The global configuration or structure of an object in a frame can be expressed as a probability density function constructed using relational attributes between low-level features, e.g. edge pixels that are extracted from the regions of interest. The probability density changes with motion, tracing a trajectory in the latent space of distributions, which we call the configuration space. These trajectories can then be used for recognition using standard techniques such as dynamic time warping. Can these frame-wise probability functions, which usually have high dimensionality, be embedded into a low-dimensional space so that we can still estimate various meaningful probabilistic distances in the new space? Given these trajectory-based representations, can one learn models of signs in an unsupervised manner? We address these two fundamental questions in this dissertation. Existing embedding approaches do not extend easily to preserve meaningful probabilistic distances between the samples. We present an embedding framework to preserve the probabilistic distances like Chernoff, Bhattacharya, Matusita, KL or symmetric-KL based on dot-products between points in this space. It results in computational savings. We experiment with the five different probabilistic distance measures and show the usefulness of the representation in three different contexts - sign recognition of 147 different signs (with large number of possible classes), gesture recognition with 7 different gestures performed by 7 different persons (with person variations) and classification of 8 different kinds of human-human interaction sequences (with segmentation problems). Currently, researchers in continuous sign language recognition assume that the training signs are already available and often those are manually selected from continuous sentences. It consumes a lot of human time and is tedious. We present an approach for automatically learning signs from multiple sentences by using a probabilistic framework to extract the parts of signs that are present in most of its occurrences, and are robust to variations produced by adjacent signs. We show results by learning 10 signs and 10 spoken words from 136 sign language sentences and 136 spoken sequences respectively.

abstract: Daily collaborative tasks like pushing a table or a couch require haptic communication between the people doing the task. To design collaborative motion planning algorithms for such applications, it is important to understand human behavior. Collaborative tasks involve continuous adaptations and intent recognition between the people involved in the task. This thesis explores the coordination between the human-partners through a virtual setup involving continuous visual feedback. The interaction and coordination are modeled as a two-step process: 1) Collecting data for a collaborative couch-pushing task, where both the people doing the task have complete information about the goal but are unaware of each other's cost functions or intentions and 2) processing the emergent behavior from complete information and fitting a model for this behavior to validate a mathematical model of agent-behavior in multi-agent collaborative tasks. The baseline model is updated using different approaches to resemble the trajectories generated by these models to human trajectories. All these models are compared to each other. The action profiles of both the agents and the position and velocity of the manipulated object during a goal-oriented task is recorded and used as expert-demonstrations to fit models resembling human behaviors. Analysis through hypothesis teasing is also performed to identify the difference in behaviors when there are complete information and information asymmetry among agents regarding the goal position. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020