119007
Categorical representation and recognition of oscillatory motion patterns
Many communicative behaviors in the animal kingdom consist of performing and recognizing specialized patterns of oscillatory motion. Here we present an approach to the representation and recognition of these oscillatory motions based on the categorical organization of a simple sinusoidal model having very specific and limited parameter values. This characterization is used to specify the types and layout of computation for recognizing the patterns. Results of the method are demonstrated with real oscillatory motions showing the viability of a structured categorical framework.
18551439
An adaptive focus-of-attention model for video surveillance and monitoring
In current video surveillance systems, commercial pan/tilt/zoom (PTZ) cameras typically provide naive (or no) automatic scanning functionality to move a camera across its complete viewable field. However, the lack of scene-specific information inherently handicaps these scanning algorithms. We address this issue by automatically building an adaptive, focus-of-attention, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed by first detecting local human activity (i.e., any translating object with a specific temporal signature) at discrete locations across a PTZ camera’s entire viewable field. The temporal signature of translating objects is extracted using motion history images (MHIs) and an original, efficient algorithm based on an iterative candidacy-classification-reduction process to separate the target motion from noise. The target motion at each location is then quantified and employed in the construction of a global activity map for the camera. We additionally present four new camera scanning algorithms which exploit this activity map to maximize a PTZ camera’s opportunity of observing human activity within the camera’s overall field of view. We expect that these efficient and effective algorithms are implementable within current commercial camera systems.
14802063
Minimal-latency human action recognition using reliable-inference
We present a probabilistic reliable-inference framework to address the issue of rapid detection of human actions with low error rates. The approach determines the shortest video exposures needed for low-latency recognition by sequentially evaluating a series of posterior ratios for different action classes. If a subsequence is deemed unreliable or confusing, additional video frames are incorporated until a reliable classification to a particular action can be made. Results are presented for multiple action classes and subsequence durations, and are compared to alternative probabilistic approaches. The framework provides a means to accurately classify human actions using the least amount of temporal information.
1243996
Real-time closed-world tracking
A real-time tracking algorithm that uses contextual information is described. The method is capable of simultaneously tracking multiple, non-rigid objects when erratic movement and object collisions are common. A closed-world assumption is used to adaptively select and weight image features used for correspondence. Results of algorithm testing and the limitations of the method are discussed. The algorithm has been used to track children in an interactive, narrative playspace.
11066432
Perceptual user interfaces: the KidsRoom
T he KidsRoom is a fully automated and interactive narrative playspace for children developed at the MIT Media Laboratory. Built to explore the design of perceptually based interactive interfaces, the Kids-Room uses computer vision action recognition simultaneously with computerized control of images, video, light, music, sound, and narration to guide children through a storybook adventure. Unlike most previous work in interactive environments, the Kids-Room does not require people in the space to wear any special clothing or hardware, and the KidsRoom can accommodate up to four people simultaneously. The system was designed to use computational perception to keep most interaction in the real, physical space even as participants interacted with virtual characters and scenes. The KidsRoom, designed in the spirit of several popular children's books, is an interactive child's bedroom that stimulates imagination by responding to actions with images and sound to transform itself into a storybook world. Two of the bedroom walls resemble the real walls in a child's room, complete with real furniture, posters, and windows. The other two walls are large, back-projected video screens used to transform the appearance of the room environment. Four speakers and one amplifier project steerable sound effects, music, and narration into the space. Three video cameras overlooking the space provide input to computer vision people-tracking and action recognition algorithms. Computer-controlled theatrical lighting illuminates the space, and a microphone detects the volume of enthusiastic screams. The room is fully automated. During the story, children interact with objects in the room, with one another, and with virtual creatures projected onto the walls. Perceptual recognition makes it possible for the room to respond to the physical actions of the children by appropriately moving the story forward thereby creating a compelling interactive narrative experience. Conversely, the narrative context of the story makes it easier to develop context-dependent (and therefore more robust) action recognition algorithms. The story developed for the KidsRoom begins with a normal-looking bedroom. Children enter after being told to find out the magic word by asking the talking furniture that speaks when approached. When the children scream the magic word loudly, sounds and images transform the room into a mystical forest. The story narration prods the children to stay in a group and follow a path to a river (see the stone path (a) in the figure). Along the way, they encounter roaring monsters and must hide behind the bed to make the roars …
18888326
Analysis and recognition of walking movements
We present an approach for recognizing human walking movements using low-level motion regularities and constraints. Biomechanical features for classification are automatically extracted from video sequences of walkers. A multiplicative classification rule using statistical distances is then used to determine whether an unknown motion is consistent with normal walking patterns. Recognition results are shown distinguishing walking examples across multiple speeds from other nonwalking locomotions.
17135898
The Economic Contribution of Software: An Alternative Perspective on the Productivity Paradox
The complex relationships between information technology investments and business value have been the focus of intensive research in recent years. There appears to be a discernible trend toward a more nuanced view in which the differential effects of the various categories of IT capital such as hardware, software, and their interactions with organizational factors are systematically investigated. As well, there is emerging evidence of accelerating investments in software and a greater shift toward “softwarization” (Langdon 2003) in which value addition is linked to combining flexible software with increasingly commoditized hardware. In this paper, we focus on the differential contributions of hardware and software capital and their interactions with labor capital. We use industry-level data to extend previous studies in three ways: (1) by using more recent data (1990 to 2002), (2) by focusing on IT-using industries in the private sector, and (3) by treating IT hardware and IT software as two distinct classes of IT capital. We adopt the commonly used log-linear Cobb Douglas production function approach. Our findings indicate that the impacts of software are significantly different in comparison with hardware and that the productivity benefits attributable to IT are largely due to the interactions between software and labor inputs. We conclude that software is the key to productivity growth in the IT-using world, and show that it can be used as the closest surrogate to represent business complementarities to IT in macro-level studies.
10174533
Cost-Sensitive Decision Tree Learning for Forensic Classification
In some learning settings, the cost of acquiring features for classification must be paid up front, before the classifier is evaluated. In this paper, we introduce the forensic classification problem and present a new algorithm for building decision trees that maximizes classification accuracy while minimizing total feature costs. By expressing the ID3 decision tree algorithm in an information theoretic context, we derive our algorithm from a well-formulated problem objective. We evaluate our algorithm across several datasets and show that, for a given level of accuracy, our algorithm builds cheaper trees than existing methods. Finally, we apply our algorithm to a real-world system, Clarify. Clarify classifies unknown or unexpected program errors by collecting statistics during program runtime which are then used for decision tree classification after an error has occurred. We demonstrate that if the classifier used by the Clarify system is trained with our algorithm, the computational overhead (equivalently, total feature costs) can decrease by many orders of magnitude with only a slight (<1%) reduction in classification accuracy.
7180811
Calibrating pan-tilt cameras in wide-area surveillance networks
Pan-tilt cameras are often used as components of wide-area surveillance systems. It is necessary to calibrate these cameras in relation to one another in order to obtain a consistent representation of the entire space. Existing methods for calibrating pan-tilt cameras have assumed an idealized model of camera mechanics. In addition, most methods have been calibrated using only a small range of camera motion. We present a method for calibrating pan-tilt cameras that introduces a more complete model of camera motion. Pan and tilt rotations are modeled as occurring around arbitrary axes in space. In addition, the wide area surveillance system itself is used to build a large virtual calibration object, resulting in better calibration than would be possible with a single small calibration target. Finally, the proposed enhancements are validated experimentally, with comparisons showing the improvement provided over more traditional methods.
29147523
SCAPE: shape completion and animation of people
We introduce the SCAPE method (Shape Completion and Animation for PEople)---a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations. We learn a pose deformation model that derives the non-rigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion --- generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.
24382165
Spacetime Stereo: A Unifying Framework for Depth from Triangulation
Depth from triangulation has traditionally been investigated in a number of independent threads of research, with methods such as stereo, laser scanning, and coded structured light considered separately. We propose a common framework called spacetime stereo that unifies and generalizes many of these previous methods. To show the practical utility of the framework, we develop two new algorithms for depth estimation: depth from unstructured illumination change and depth estimation in dynamic scenes. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.