Derek T. Anderson

Linguistic summarization of video for fall detection using voxel person and fuzzy logic

In this paper, we present a method for recognizing human activity from linguistic summarizations of temporal fuzzy inference curves representing the states of a three-dimensional object called voxel person. A hierarchy of fuzzy logic is used, where the output from each level is summarized and fed into the next level. We present a two level model for fall detection. The first level infers the states of the person at each image. The second level operates on linguistic summarizations of voxel persons states and inference regarding activity is performed. The rules used for fall detection were designed under the supervision of nurses to ensure that they reflect the manner in which elders perform these activities. The proposed framework is extremely flexible. Rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability.

Recognizing Falls from Silhouettes

A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind

Modeling Human Activity From Voxel Person Using Fuzzy Logic

As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.

Comparing Fuzzy, Probabilistic, and Possibilistic Partitions

When clustering produces more than one candidate to partition a finite set of objects O, there are two approaches to validation (i.e., selection of a “best” partition, and implicitly, a best value for c , which is the number of clusters in O). First, we may use an internal index, which evaluates each partition separately. Second, we may compare pairs of candidates with each other, or with a reference partition that purports to represent the “true” cluster structure in the objects. This paper generalizes many of the classical indices that have been used with outputs of crisp clustering algorithms so that they are applicable for candidate partitions of any type (i.e., crisp or soft, with soft comprising the fuzzy, probabilistic, and possibilistic cases). Space prevents inclusion of all of the possible generalizations that can be realized this way. Here, we concentrate on the Rand index and its modifications. We compare our fuzzy-Rand index with those of Campello, Hullermeier and Rifqi, and Brouwer, and show that our extension of the Rand index is O(n), while the other three are all O(n2). Numerical examples are given to illustrate various facets of the new indices. In particular, we show that our indices can be used, even when the partitions are probabilistic or possibilistic, and that our method of generalization is valid for any index that depends only on the entries of the classical (i.e., four-pair types) contingency table for this problem.

Speedup of Fuzzy Clustering Through Stream Processing on Graphics Processing Units

As the number of data points, feature dimensionality, and number of centers for clustering algorithms increase, computational tractability becomes a problem. The fuzzy c-means has a large degree of inherent algorithmic parallelism that modern CPU architectures do not exploit. Many pattern recognition algorithms can be sped up on a graphics processing unit (GPU) as long as the majority of computation at various stages and the components are not dependent on each other. We present a generalized method for offloading fuzzy clustering to a GPU, while maintaining control over the number of data points, feature dimensionality, and the number of cluster centers. GPU-based clustering is a high-performance low-cost solution that frees up the CPU. Our results show a speed increase of over two orders of magnitude for particular clustering configurations and platforms.

Estimation of adult skeletal age-at-death using the Sugeno fuzzy integral

Age-at-death estimation of an individual skeleton is important to forensic and biological anthropologists for identification and demographic analysis, but it has been shown that the current aging methods are often unreliable because of skeletal variation and taphonomic factors. Multifactorial methods have been shown to produce better results when determining age-at-death than single indicator methods. However, multifactorial methods are difficult to apply to single or poorly preserved skeletons, and they rarely provide the investigator with information about the reliability of the estimate. The goal of this research is to examine the validity of the Sugeno fuzzy integral as a multifactorial method for modeling age-at-death of an individual skeleton. This approach is novel because it produces an informed decision of age-at-death utilizing multiple age indicators while also taking into consideration the accuracies of the methods and the condition of the bone being examined. Additionally, the Sugeno fuzzy integral does not require the use of a population and it qualitatively produces easily interpreted graphical results. Examples are presented applying three commonly used aging methods on a known-age skeletal sample from the Terry Anatomical Collection. This method produces results that are more accurate and with smaller intervals than single indicator methods.

Using a hand-drawn sketch to control a team of robots

In this paper, we describe a prototype interface that facilitates the control of a mobile robot team by a single operator, using a sketch interface on a Tablet PC. The user draws a sketch map of the scene and includes the robots in approximate starting positions. Both path and target position commands are supported as well as editing capabilities. Sensor feedback from the robots is included in the display such that the sketch interface acts as a two-way communication device between the user and the robots. The paper also includes results of a usability study, in which users were asked to perform a series of tasks.

Combination of Anomaly Algorithms and Image Features for Explosive Hazard Detection in Forward Looking Infrared Imagery

A novel approach is proposed for combining multiple anomaly algorithm decisions with image space cell-structured features in a long wave infrared (LWIR) system in the context of forward looking (FL) buried explosive hazard detection along a road. A pre-screener is applied first, which is an ensemble of trainable size-contrast filters with mean shift clustering in Universal Transverse Mercator (UTM) space. Next, features from image chips representing anomaly decisions from different algorithms are extracted from UTM confidence maps based on maximally stable extremal regions (MSERs) and Gaussian mixture models (GMMs). Pre-screener hits in UTM space are back-projected into the video at multiple standoff distances and cell-structured local binary patterns (LBPs), histogram of gradients (HOGs) and mean-variance descriptors are extracted. Experiments are conducted using buried materials with varying metal contents and depths at a U.S. Army test site. Results are extremely encouraging for FL imaging and show a significant decrease in the number of false alarms (FAs). Targets not currently detected by our system are also not detected by a human under manual visual inspection.

Learning fuzzy-valued fuzzy measures for the fuzzy-valued Sugeno fuzzy integral

Fuzzy integrals are very useful for fusing confidence or opinions from a variety of sources. These integrals are non-linear combinations of the support functions with the (possibly subjective) worth of subsets of the sources, realized by a fuzzy measure. There have been many applications and extensions of fuzzy integrals and this paper deals with a Sugeno integral where both the integrand and the measure take on fuzzy number values. A crucial aspect of using fuzzy integrals for fusion is determining or learning the measures. Here, we propose a genetic algorithm with novel cross-over and mutation operators to learn fuzzy-valued fuzzy measures for a fuzzy-valued Sugeno integral.

Linguistic summarization of long-term trends for understanding change in human behavior

In this paper, we propose a linguistic summarization procedure for describing long-term trends of change in human behavior. Our objective consists of defining methods that provide information to elders, caregivers, social workers or even family in an understandable language. We adapt a measure that we defined in previous work on soft cluster partition similarity for comparing behaviors that are adapted over time. From that measure, we are able to produce a time series that numerically describes change in behavior over time. In this article, the resulting time series is partitioned and linguistically summarized depending on a users (caregiver, social worker, etc.) desired time resolution. Simulated resident behavior is used in order to explore a range of different scenarios and the response of the proposed linguistic summarization process is investigated.