1 Introduction

This dissertation presents sound localization and color vision techniques working together with target tracking and fuzzy behaviors for real-time automatic camera control. This system follows people with a pan-tilt-zoom camera for potential surveillance and videoconferencing applications. The strategy employed combines simple audio and visual sensory cues through a common spatial representation in order to extract likely target locations. For visual cues, skin tone color and motion are detected; while inter-aural time delay between stereo microphones provides directional sound information. The choices of sensory cues and fusion scheme reduce a potentially complex sensor interpretation problem into one that is practical for real-time tracking applications implemented on a personal computer.

Equally important to the task of sensor interpretation is the manner in which the interpretation is used. This work also presents the use of behavior-based fuzzy control techniques to direct a pan-tilt-zoom camera as required by a videoconferencing or surveillance application. Fuzzy control is a useful technique for defining complex control functions according to subjective expert rules described by human beings. The behavior-based approach used in this work was borrowed from previous mobile robot research, which required robust autonomous operation despite noisy and incomplete sensor information. The problems faced by the robot and autonomous camera system are quite similar, allowing many of the same fuzzy behavior concepts to be applied.

 

1.1 Objectives

The major objectives of this research are:

  1. To show that fusion of stereo sound and color video information via a common spatial representation early in processing improves a system's ability to detect and track people while reducing computational requirements; and
  2. To combine the sensor interpretation capability with behavior-based active camera control, for realistic applications such as automated videoconferencing and surveillance.

The reasons behind these objectives and the approach used to accomplish them are discussed in the remainder of this chapter.

 

1.2 Significance of the Research

The research presented in this dissertation extends the perceptual capabilities of a personal computer to allow detection and tracking of one or more people speaking. Video and sound information are interpreted by the computer to determine the location of a speaker's face in a room without requiring the intentional cooperation of the subject. This capability advances the field of human-computer interaction because it:

The most immediate application of the techniques described in this work is automatic camera control for videoconferencing. Videoconferencing has become widely popular means of collaboration between individuals or large groups, as in education. To effectively aim the video camera at the speaker for close-up views, the group has the option of hiring a camera operator, manually moving the camera, or forcing the speaker to move into the camera's field of view. The first may not be economically feasible, and the latter two can make the conference awkward. It is therefore desirable to automatically aim the camera at the person speaking. The system described in this dissertation achieves this by localizing the direction of sound and adjusting the pan, tilt, and zoom of the camera to frame the face of the speaker at that location.

There is already a significant body of literature on the subjects of sound localization, active vision, face recognition, and to a lesser extent, face detection. This dissertation improves upon previous research in the following ways.

 

 

 

 

1.3 Inspiration

The integrated use of vision and hearing is a fundamental skill for most animals in nature. Combining the spatial cues provided by these modalities is often essential for a creature to locate a predator, prey, or mate. Sound information cues the animal to redirect its vision toward new areas of interest, and vision allows the association of sounds with discrete objects in the world. This low level of sensor fusion is what enables higher level skills such as sound recognition, and should be considered a basic building block for the development of artificially intelligent systems that incorporate sound and vision.

Traditional approaches to sensor-based AI involve the extraction of symbolic representations from images and sound separately, such as visual detection of an object in a static image and recognition of words in speech. Such systems integrate multimodal sensor information by reasoning at the symbolic level, e.g. "which of the objects in the world model said this word?" The work presented here, however, investigates the fusion of multimodal information at the pixel level. By performing an intersection operation between acoustic and visual data superimposed on a common cellular representation, a reduced number of potential targets needs to be extracted and processed at the symbolic level. The resulting reduction in computational requirements allows improvement in the real-time detection and tracking of human activity.

Neurobiology research provides evidence that a cellular spatial representation is useful for fusing multimodal sensor information. The neural audio-visual processing mechanisms used by the barn owl, for example, have been extensively mapped by biologists. As explained by Knudsen [1, 2], auditory and visual information in the barn owl (and many other animals) converges at the optic tectum, where a receptive field of spatially distributed neurons responds to stimuli on the basis of the direction to corresponding sources. This structure can easily associate acoustic events with corresponding visual targets due to the proximity of vision and sound signals in the neural array. Knudsen, du Lac, and Esterly [3] describe this neural topography as a "computational map" that provides a direct representation for spatial information. A cellular map efficates fusion of multiple sensor modalities, and can efficiently cross-reference target sensation to appropriate motor actions for directional perception and pursuit behaviors.

Figure 1: Audio-Visual Sensor Fusion in the Barn Owl

Figure 1 provides an artistic conception of how computational maps in the owl can improve target detection. Light reflected from the environment creates an image on the retina. The retina and other map-like neural structures in the eye preprocess the image to enhance motion and texture information, but preserve the image's two-dimensional spatial distribution as they pass the signals on to the optic tectum in the brain. Meanwhile, the owl's ears receive temporal signals that differ in arrival times and frequency spectra depending on the direction of the sound source [4]. These differences are mapped to target azimuth and elevation at the external nucleus of the inferior colliculus (ICx). This structure consists of a spatial array of neurons that respond to sound stimuli depending on direction -- a low-resolution "image," if you will, of sound origin. Signals from these neurons pass to the optic tectum where they may be processed in spatial registration with visual information. This arrangement allows the joint stimulation of acoustic and visual neurons corresponding to the same direction in space in order to distinguish a noisy, moving target against a background of other moving targets and uncorrelated noise. The appropriate intersection of two sensing modalities at a cellular "pixel" level improves the recognition of targets with multimodal features.

In the owl, the rapid detection and localization of targets provided by computations in the optic tectum can be very quickly mapped to appropriate motor signals for tracking via head movements. (See Footnote 1) In both animals and robots, exploratory and target-tracking behaviors need not have a fully comprehensible and accurate model of the world in order to function (See Footnote 2); however, they rely on timely sensor information to estimate position and velocity. Detection speed is therefore essential, which benefits from effective incorporation of each sensing modality early in processing. The apparent advantages of computational maps in animals have inspired analogous application to sensing and control problems in robotics. Pearson, Gelfand, Sullivan, Peterson, and Spence [5] apply computational maps to multimodal sensor systems for adaptive sensor registration. Similarly, the research presented in this dissertation applies this concept to audio-visual fusion for real-time camera control. 

 

1.4 Feasibility

Recent trends in the personal computer industry have resulted in a drastic reduction in the cost capturing color video and stereo sound. In fact, many consumer PCs now come with these multimedia capabilities as standard features, and the number of people using computers for applications such as videoconferencing is growing quickly as PC processing capabilities improve. However, with the exception of some speech recognition programs, today's multimedia applications do not interpret the content of the multisensor data they receive; instead, they simply record and play back information to the user. This dissertation demonstrates how off-the-shelf multimedia PC hardware can be used for an interactive real-time camera control task simply by providing appropriate software.

Consumer-grade NTSC video capture hardware on a PC is not the type of platform typically used for computer vision research. Compared to high-resolution, fast-processing professional RGB imaging systems, the PC hardware in this research is slow, low-resolution, and offers less faithful reproduction of color. Multimedia sound cards are not typically of the high grade used for signal processing research, either. But the advantage of the multimedia personal computer is its ability to provide software with access to sound and video data on the same platform, with very high registration in time. This avoids the problems of networking together separate processing platforms for sound, video, and control, and allows tighter integration of sensor fusion and behavior algorithms. Furthermore, the algorithms described in this work do not require high-resolution full frame-rate video or high computational capability. They perform adequately with low-resolution, low frame-rate color video, CD-quality sound capture, and a modest CPU.

The risks faced by failure of a video camera control system are relatively benign. Failure means that the camera would be pointed in an inappropriate direction, and for most applications such as videoconferencing and surveillance, there is no direct damage to health or property. The cost is most likely to be a temporary annoyance to the human viewer. This is dramatically different from sensor-based military tracking, vehicle navigation, and industrial inspection and sorting systems which depend on very reliable performance. Low cost and low risk allows us to experiment with suboptimal sensing and control schemes that degrade gracefully in performance until they fail, but when they do so, fail cognizantly and recover. The simple sensing strategy and behavior-based control scheme presented in this dissertation provides useful performance with computational demands low enough to be implemented with inexpensive consumer-grade hardware.

 

1.5 Approach

An experimental test-bed was configured using multimedia PC components to test sound localization, color vision, sensor fusion, and target tracking algorithms. For accurate fusion of spatial sensor information, proper alignment among the sensors is required. Figure 2 shows the configuration of sensors used for this system, including two electret microphones spaced 30 cm apart, a wide-angle color camera, and a computer-controlled pan-tilt-zoom color camera. The entire sensor assembly is mounted on a tripod next to the computer.

Figure 2: Sensor Configuration

The wide-angle camera's fish-eye lens gives it a field of view 92 degrees wide, allowing it to see nearly an entire room at once. The Canon VC-C1 camera is designed for videoconferencing, and features computer-controllable pan, tilt, and zoom features. The VC-C1 is used for close-up views of people for videoconferencing or surveillance. Either camera may be used for vision tasks, as they have different advantages. Note that the wide-angle camera is not necessary for the videoconferencing application described in this work, but it may be exploited to improve performance when detecting or tracking targets over a large area in a surveillance application.

Figure 3: Component Interconnections

A diagram of sensor, data acquisition, and processing components is shown in Figure 3. The computer is a 66 MHz 586-class PC running OS/2. (See Footnote 3) An inexpensive multimedia video capture card is used to grab 320 x 240 pixel color images from either camera, and a sound card equipped with an IBM IMDSP2780 MwaveTM chip is used to record and process stereo 16-bit 44.1 kHz sampled digital sound. Since the 2780 chip is fully programmable in C or assembly, all audio processing can be performed on board the card in parallel with tasks running on the host CPU. Image processing, sensor fusion, and camera control are performed in real-time on the host x86-class processor.

Figure 4: Data Flow Block Diagram

A block diagram of the processing steps performed by the system is shown in Figure 4. Sound processing performed on the Mwave card preprocesses and cross-correlates sound signals from the left and right microphones to create a histogram relating sound power to azimuth. Meanwhile, the host CPU captures video from one (or even both) of the cameras, detecting motion and/or skin tone color. The sensor fusion task then combines the color information with the sound histogram when selecting which pixels are likely to be occupied by targets of interest. The resulting image is segmented, and regions are tracked using Kalman filtering in global Cartesian coordinates. Lastly, a fuzzy behavior-based control system interprets the sound and tracking data to control the pan-tilt-zoom camera.

This chapter has presented the motivation behind this dissertation's research and the laboratory setup used for conducting experiments. The use of co-aligned camera and microphone sensors attached to a multimedia computer results in a convenient platform for audiovisual sensor fusion research, providing spatially and temporally registered multimodal data. The active pan-tilt-zoom camera provides the ability to actively explore the environment and respond to human beings in real time. In the next chapter, this research will be compared to related work in the literature.