A person tracking system for interactive environments has several desired criteria: it should operate in real-time, be robust to multiple users and changing background, provide a relatively rich visual description of the users, and be able to track people when they are occluded or momentarily leave the scene. We achieve these goals through the use of multi-modal integration and multi-scale temporal tracking.
We base our system on three primary visual processing modules: depth estimation, color segmentation, and intensity pattern classification (see Figure 1). As described in more detail below, depth information is estimated using a dense real-time stereo technique and allows easy segmentation of the user from other people and background objects. An intensity-invariant color classifier detects regions of flesh tone on the user and is used to identify likely body part regions such as face and hands. A face detection module is used to discriminate head regions from hands and other tracked body parts.
Figure 2 shows the output of the three vision processing modules. As a person tracker, each is individually fragile: notebooks are indistinguishable from faces in range silhouette, flesh color signs or clothes fool color-only trackers, and face pattern detectors typically are slower and only work with relatively canonical poses and expressions. However, when integrated together these modules can yield robust, fast tracking performance.
Tracking is performed in our system on three different time-scales: short-range (frame to frame while the person is visible), medium-range (when the person is momentarily occluded or leaves the field of view for a few minutes), and long range (when the person is absent for hours, days or more.) Long-term tracking can be thought of as a person identification task, where the database is formed from the set of previous users. For short-term tracking we simply compute region correspondences specific to each processing modality based on region position and size. Multi-modal integration is performed using the history of short-term tracked regions from each modality, yielding a representation of the user's body shape and face location.
For medium and long-range tracking, we rely on a statistical model of multi-modal appearance to resolve correspondences between tracked users. In addition to body shape and face location, and color of hair, skin, and clothes is recorded at each time step. We record the average value and covariance of represented features, and use them for matching. For medium-term tracking, lighting constancy and stable clothing color are assumed; for long-term tracking we adjust for changing lighting and do not include clothing in the match criteria.
In the next section, we discuss module specific processing, including classification, segmentation/grouping, and short-term tracking. Following that, we present our integration scheme, and correspondence method for medium and long-term tracking.