Clinical studies confirm that mental illnesses such as autism, Obsessive Compulsive Disorder (OCD), etc. show behavioral abnormalities even at very young ages; the early diagnosis of which can help steer effective treatments. Most often, the behavior of such at-risk children deviate in very subtle ways from that of a normal child; correct diagnosis of which requires prolonged and continuous monitoring of their activities by a clinician, which is a difficult and time intensive task. As a result, the development of automation tools for assisting in such monitoring activities will be an important step towards effective utilization of the diagnostic resources. In this paper, we approach the problem from a computer vision standpoint, and propose a novel system for the automatic monitoring of the behavior of children in their natural environment through the deployment of multiple non-invasive sensors (cameras and depth sensors). We provide details of our system, together with algorithms for the robust tracking of the activities of the children. Our experiments, conducted in the Shirley G. Moore Laboratory School, demonstrate the effectiveness of our methodology.

In developmental disorders such as autism and schizophrenia, observing behavioral precursors in very early childhood can allow for early intervention and can improve patient outcomes. While such precursors open the possibility of broad and large-scale screening, until now they have been identified only through experts' painstaking examinations and their manual annotations of limited, unprocessed video footage. Here we introduce a system to automate and assist in such procedures. Employing multiple inexpensive real-time rgb+depth (rgb+d) sensors recording from multiple viewpoints, our non-invasive system - now installed at the Shirley G. Moore Lab School, a research preschool - is being developed to monitor and reconstruct the play and interactions of preschoolers. The system's role is to help in assessing the growing volumes of its on-site recordings and to provide the data needed to uncover additional neuromotor behavioral markers via techniques such as data mining.

The literature on distributed robotic teams working in adversarial settings focuses primarily on external entities attempting to thwart the team. The question is raised, however, about what can be done when the adversary is within the team itself? That is, if one team member turns traitor? This paper presents an initial investigation into this question. A method is developed which can be used to classify the behavior of other team members, and provide a measure of how similar their behavior is to the expected behavior. A simulation and a real-world experiment are presented, and results show that expected and traitorous behavior are distinguishable in an example real world setting. I.

In order to increase the effectiveness of robotic systems, the robots must be able to negotiate a variety of terrains. In urban environments this task becomes challenging as environments built for humans often have impediments such as stairs and thresholds which may be difficult or impossible for wheeled or tracked vehicles to go over. This is further complicated in search and rescue scenarios where debris and rubble from collapsed structures may further complicate the environment. In order to address these limitations a novel robotic platform, the Loper, has been developed. The Loper utilizes a Tri-lobe wheel and a compliant chassis in order to traverse difficult terrain. In addition, multiple gait configurations of the Tri-lobe wheel enable the Loper to overcome a variety of obstacles, both indoors and out.

This paper presents a mathematical basis for judging the quality of camera and projector placement in 3D for structured light systems. Two important quality metrics are considered: visibility, which measures how much of the target object is visible; and scale, which measures the error in detecting the visible portions. A novel method for computing each of these metrics is presented. An example is discussed which demonstrates use of these two metrics. The proposed techniques have direct applicability to the task of monitoring patient safety for radiation therapy applications.

This work presents a method for detecting abandoned objects in real-world conditions. The method presented here addresses the online and real time aspects of such systems, utilizes logic to differentiate between abandoned objects and stationary people, and is robust to temporary occlusion of potential abandoned objects. The capacity to not detect still people as abandoned objects is a major aspect that differentiates this work from others in the literature. Results are presented on 3 hours 36 minutes of footage over four videos representing both sparsely and densely populated real-world situations, also differentiating this work from others in the literature

This paper presents a vision-based method to automatically detect individuals loitering about inner-city bus stops. Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time; this system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, time stamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using a 30-min video of a busy bus stop with six individuals loitering about it. Results show that the system successfully classifies images of all six individuals as loitering.

We introduce a vision-based system to monitor for suspicious human activities at a bus stop. The system currently examines for drug dealing activity. To accomplish this goal, the system must measure how long individuals loiter around the bus stop. To facilitate this, the system must track individuals from the video feed, identify them, and keep a record of how long they spend at the bus stop. The system is broken into three distinct portions: background subtraction, object tracking, and human recognition. The background subtraction and object tracking modules use off-the-shelf algorithms and are shown to work well following people as they walk around a bus stop. The human recognition module segments the image of an individual into three portions corresponding to the head, torso, and legs. Using the median color of each of these regions, two people can be quickly compared to see if they are the same person.