In this article, we present the Contextual Activity-Notification Visualization Analysis System (Canvas), which is used to develop advanced monitoring techniques, integrate cameras installed around the University of California, San Diego (UCSD) campus and centralize information. Our work focuses on building an upgradeable framework for simple user interaction through an accessible visualization. Rather than present a user with raw sensor data from the physical world, we introduce visualization layers to abstract the internals of monitoring algorithms and provide a clean consumable computational output. Canvas provides a flexible backbone that lets us improve vision algorithms while providing a seamless visualization interface. This ultimately improves the effectiveness of the monitoring by focusing attention and presenting only the most relevant information. The visualization is built on Web technology to make the information available anywhere, anytime.

Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented.

Intelligent environments can be viewed as systems where humans and machines (rooms) collaborate. Intelligent (or smart) environments need to extract and maintain an awareness of a wide range of events and human activities occurring in these spaces. This requirement is crucial for supporting efficient and effective interactions among humans as well as humans and intelligent spaces. Visual information plays an important role for developing accurate and useful representation of the static and dynamic states of an intelligent environment. Accurate and efficient capture, analysis, and summarization of the dynamic context requires the vision system to work at multiple levels of semantic abstractions in a robust manner. In this paper, we present details of a long-term and ongoing research project, where indoor intelligent spaces endowed with a range of useful functionalities are designed, built, and systematically evaluated. Some of the key functionalities include: intruder detection; multiple person tracking; body pose and posture analysis; person identification; human body modeling and movement analysis; and for integrated systems for intelligent meeting rooms, teleconferencing, or performance spaces. The paper includes an overall system architecture to support design and development of intelligent environments. Details of panoramic (omnidirectional) video camera arrays, calibration, video stream synchronization, and real-time capture/processing are discussed. Modules for multicamera-based multiperson tracking, event detection and event based servoing for selective attention, voxelization, streaming face recognition, are also discussed. The paper includes experimental studies to systematically evaluate performance of individual video analysis modules as well as to evaluate basic feasibility of an integrated system for dynamic context capture and event based servoing, and semantic information summarization.

Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points, causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) and qualitative metrics (scene and object independence, flexibility to shadow situations, and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated "ground-truth" data are made available at http://cvrr.ucsd.edu/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.

In this paper, we introduce a novel laser-based wide-area heads-up windshield display which is capable of actively interfacing with a human as part of a driver assistance system. The dynamic active display (DAD) is a unique prototype interface that presents safety-critical visual icons to the driver in a manner that minimizes the deviation of his or her gaze direction without adding to unnecessary visual clutter. As part of an automotive safety system, the DAD presents alerts in the field of view of the driver only if necessary, which is based upon the state and pose of the driver, vehicle, and environment. This paper examines the effectiveness of DAD through a comprehensive comparative experimental evaluation of a speed compliance driver assistance system, which is implemented on a vehicular test bed. Three different types of display protocols for assisting a driver to comply with speed limits are tested on actual roadways, and these are compared with a conventional dashboard display. Given the inclination, drivers who are given an overspeed warning alert reduced the time required to slow down to the speed limit by 38% (p < 0.01) as compared with the drivers not given the alert. Additionally, certain alerts decreased distraction levels by reducing the time spent looking away from the road by 63% (p < 0.01). Ultimately, these alerts demonstrate the utility and promise of the DAD system.

Dynamic analysis of vehicle occupant posture is a key requirement in designing "smart airbag" systems. Vision-based technology could enable the use of precise information about the occupant's size, posture, and, in particular, position in making airbag-deployment decisions. Novel sensory systems and algorithms need to be. developed for capture, analysis, and classification of dynamic video-based information for a new generation of safe airbags. This paper presents a systematic investigation in which stereo and thermal long-wavelength infrared video-based real-time vision systems are developed and systematically evaluated. It also includes the design of several test beds, including instrumented vehicles for systematic experimental studies for the evaluation of independent and comparative evaluation in automobiles. Results of extensive experimental trials suggest basic feasibility of stereo and. thermal long-wavelength infrared video-based occupant position and posture-analysis system.