Nathaniel D. Bird

Detection of loitering individuals in public transportation areas

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.

Real time, online detection of abandoned objects in public areas

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

Driver activity monitoring through supervised and unsupervised learning

This paper presents two different learning methods applied to the task of driver activity monitoring. The goal of the methods is to detect periods of driver activity that are not safe, such as talking on a cellular telephone, eating, or adjusting the dashboard radio system. The system presented here uses a side-mounted camera looking at a drivers profile and utilizes the silhouette appearance obtained from skin-color segmentation for detecting the activities. The unsupervised method uses agglomerative clustering to succinctly represent driver activities throughout a sequence, while the supervised learning method uses a Bayesian eigen-image classifier to distinguish between activities. The results of the two learning methods applied to driving sequences on three different subjects are presented and extensively discussed.

A multi-sensor visual tracking system for behavior monitoring of at-risk children

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.

Human activities monitoring at bus stops

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.

Using Robots to Raise Interest in Technology Among Underrepresented Groups

Women and minorities are under represented in the IT field at the high school, university, and industry levels. Efforts to address this imbalance are often too late to solve underlying problems such as perceived ineptitude and actual inexperience. By designing and hosting a program for these underrepresented students in the middle grades, the Center for Distributed Robotics at the University of Minnesota hopes to establish a successful annual robotics day camp that will inspire both women and minorities to pursue careers in technology. Detailed accounts of the goals and methodology are provided. Initial survey results reveal a very positive response from the campers as well as strengths and weaknesses that will be useful in designing or refining similar camps.

Optimal Image-Based Euclidean Calibration of Structured Light Systems in General Scenes

This paper presents a method to perform Euclidean calibration on camera and projector-based structured light systems, without assuming specific scene structure. The vast majority of the methods in the literature rely on prior knowledge of the 3D scene geometry in order to perform calibration, i.e., the nature of the occluding bodies in the scene needs to be known beforehand in order to calibrate structured light systems. Examples of prior knowledge used include using known stationary occluding bodies, precisely maneuvering known occluding bodies, knowing the exact world location of projected points or lines, or ensuring the entire scene obeys some other specific setup. By using multiple cameras, the method presented in this paper is able to calibrate camera and projector systems without requiring any of these constraints on occluding bodies in the scene. The method presented optimizes the calibration of the scene in terms of image-based reprojection error. Simulations are shown which characterize the effect noise has on the system, and experimental verification is performed on complex and cluttered scenes. The main contribution of this paper is the elimination of the requirement of using known occluding bodies in the scene for camera and projector-based structured light system calibration, which has not been extensively studied.

A search and rescue robot

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.

Placement quality in structured light systems

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.

Recognition of traitors in distributed robotic teams

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.