11408137
Building Adaptive Camera Models for Video Surveillance
We address the limited automatic scanning functionality of standard PTZ camera systems. We present an adaptive, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed automatically by detecting human activity in motion history images (MHIs) using an iterative candidacy-classification-reduction process. The target motion is quantified and employed in the construction of a global activity map, which in turn is used to direct or navigate the camera
16142452
Differential video coding of face and gesture events in presentation videos
Currently, bandwidth limitations pose a major challenge for delivering high-quality multimedia information over the Internet to users. In this research, we aim to provide a better compression of presentation videos (e.g., lectures). The approach is based on the idea that people tend to pay more attention to the face and gesturing hands, and therefore these regions are given more resolution than the remaining image. Our method first detects and tracks the face and hand regions using color-based segmentation and Kalman filtering. Next, different classes of natural hand gesture are recognized from the hand trajectories by identifying gesture holds, position/velocity changes, and repetitive movements. The detected face/ hand regions and gesture events in the video are then encoded at higher resolution than the remaining lower-resolution background. We present results of the tracking and gesture recognition approach, and evaluate and compare videos compressed with the proposed method to uniform compression.
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.
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.
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.
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 …
1681184
Improved error reporting for software that uses black-box components
An error occurs when software cannot complete a requested action as a result of some problem with its input, configuration, or environment. A high-quality error report allows a user to understand and correct the problem. Unfortunately, the quality of error reports has been decreasing as software becomes more complex and layered. End-users take the cryptic error messages given to them by programsand struggle to fix their problems using search engines and support websites. Developers cannot improve their error messages when they receive an ambiguous or otherwise insufficient error indicator from a black-box software component.
 We introduce Clarify, a system that improves error reporting by classifying application behavior. Clarify uses minimally invasive monitoring to generate a behavior profile, which is a summary of the program's execution history. A machine learning classifier uses the behavior profile to classify the application's behavior, thereby enabling a more precise error report than the output of the application itself.
 We evaluate a prototype Clarify system on ambiguous error messages generated by large, modern applications like gcc, La-TeX, and the Linux kernel. For a performance cost of less than 1% on user applications and 4.7% on the Linux kernel, the proto type correctly disambiguates at least 85% of application behaviors that result in ambiguous error reports. This accuracy does not degrade significantly with more behaviors: a Clarify classifier for 81 La-TeX error messages is at most 2.5% less accurate than a classifier for 27 LaTeX error messages. Finally, we show that without any human effort to build a classifier, Clarify can provide nearest-neighbor software support, where users who experience a problem are told about 5 other users who might have had the same problem. On average 2.3 of the 5 users that Clarify identifies have experienced the same problem.
1351850
Filling holes in complex surfaces using volumetric diffusion
We address the problem of building watertight 3D models from surfaces that contain holes - for example, sets of range scans that observe most but not all of a surface. We specifically address situations in which the holes are too geometrically and topologically complex to fill using triangulation algorithms. Our solution begins by constructing a signed distance function, the zero set of which defines the surface. Initially, this function is defined only in the vicinity of observed surfaces. We then apply a diffusion process to extend this function through the volume until its zero set bridges whatever holes may be present. If additional information is available, such as known-empty regions of space inferred from the lines of sight to a 3D scanner, it can be incorporated into the diffusion process. Our algorithm is simple to implement, is guaranteed to produce manifold non-interpenetrating surfaces, and is efficient to run on large datasets because computation is limited to areas near holes.
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.
9694396
Assigning Intonational Features in Synthesized Spoken Directions
Speakers convey much of the information hearers use to interpret discourse by varying prosodic features such as PHRASING, PITCH ACCENT placement, TUNE, and PITCH RANGE. The ability to emulate such variation is crucial to effective (synthetic) speech generation. While text-to-speech synthesis must rely primarily upon structural information to determine appropriate intonational features, speech synthesized from an abstract representation of the message to be conveyed may employ much richer sources. The implementation of an intonation assignment component for Direction Assistance, a program which generates spoken directions, provides a first approximation of how recent models of discourse structure can be used to control intonational variation in ways that build upon recent research in intonational meaning. The implementation further suggests ways in which these discourse models might be augmented to permit the assignment of appropriate intonational features.