Trajectory Guided Tracking and Recognition of Actions

A combined 2D, 3D approach is presented that allows for robust tracking of moving people and recognition of actions. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object that are then used as input to action recognition modules. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture-of-Gaussians classifier. The system was tested in recognizing various dynamic human outdoor activities: running, walking, roller blading, and cycling. Experiments with real and synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

Computational Properties of SNAFU

Sensor applications in Sensoria [1] are expressed using STEP (Sensorium Task Execution Plan). SNAFU (Sensor-Net Applications as Functional Units) serves as a high-level sensor-programming language, which is compiled into STEP. In SNAFU’s current form, its differences with STEP are relatively minor, as they are limited to shorthands and macros not available in STEP. We show that, however restrictive it may seem, SNAFU has in fact universal power; technically, it is a Turing-complete language, i.e., any Turing program can be written in SNAFU (though not always conveniently). Although STEP may be allowed to have universal power, as a low-level language not directly available to Sensorium users, SNAFU programmers may use this power for malicious purposes or inadvertently introduce errors with destructive consequences. In future developments of SNAFU, we plan to introduce restrictions and highlevel features with safety guards, such as those provided by a type system, which will make SNAFU programming safer.