This paper describes the motivation, design and implementation of a Perception Validation System (PVS), a system for measuring the outdoor perception performance of an autonomous vehicle. The PVS relies on using large amounts of real world data and ground truth information to quantify performance aspects such as the rate of false positive or false negative detections of an obstacle detection system. Our system relies on a relational database infrastructure to achieve a high degree of flexibility in the type of analyses it can support. We discuss the main steps required for going from raw data to numerical estimates describing the performance of the perception system, including the generation of ground truth information and the safe speed metric we found to be most useful for comparing the perception system's outputs to the ground truth data. We present results illustrating some of the analyses that can be completed using the Perception Validation System.

Meetings are an integral part of business life for any organization. In previous work, we have developed a physical awareness system called CAMEO (camera assisted meeting event observer) to record and process the audio/visual information of a meeting. An important task in meeting understanding is to know who and how many people are attending the meeting. In this paper, we present an automatic approach to detect, track, and cluster people's faces in long video sequences. This is a challenging problem due to the appearance variability of people's faces (illumination, expression, pose,...). Two main novelties are presented: a robust real-time adaptive subspace face tracker which combines color and appearance. A temporal subspace clustering algorithm. The effectiveness and robustness of the proposed system is demonstrated over a data set of long videos (i.e. 1 hour).

Meetings are a very important part of everyday life for professionals working in universities, companies or governmental institutions. We have designed a physical awareness system called CAMEO (Camera Assisted Meeting Event Observer), a hardware/software system to record and monitor people's activities in meetings. CAMEO captures a high resolution omnidirectional view of the meeting by stitching images coming from almost concentric cameras. Besides recording capability, CAMEO automatically detects people and learns a person-specific facial appearance model (PS-FAM) for each of the participants. The PSFAMs allow more robust/reliable tracking and identification. In this paper, we describe the video-capturing device, photometric/geometric autocalibration process, and the multiple people tracking system. The effectiveness and robustness of the proposed system is demonstrated over several real-time experiments and a large data set of videos.

We present a meeting recorder infrastructure used to record and annotate events that occur in meetings. Multiple data streams are recorded and analyzed in order to infer a higher-level state of the group’s activities. We describe the hardware and software systems used to capture people’s activities as well as the methods used to characterize them.

Static cameras are pervasive in a variety of environments. However it remains a challenging problem to extract and reason about high-level features from real-time and continuous observation of an environment. In this paper, we present CAMEO, the Camera Assisted Meeting Event Observer, which is a physical awareness system designed for use by an agent-based electronic assistant. CAMEO is an inexpensive high-resolution omnidirectional vision system designed to be used in meeting environments. The multiple camera design achieves the desired high image resolution and lower cost that can be achieved when compared to traditional omnicameras that make use of a single camera and mirror solution.