Projects: 3D Human Body Tracking using temporal models

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3D Human Body Tracking using Temporal Models

In recent years, much work has been devoted to increasing the robustness of people tracking algorithms by introducing motion models. Most approaches rely on probabilistic methods, such as the popular CONDENSATION algorithm, to perform the tracking. While effective, such probabilistic approaches require exponentially large amounts of computation as the number of degrees of freedom in the model increases, and can easily become trapped into local minima unless great care is taken to avoid them.

By contrast, we use temporal motion models based on Principal Component Analysis (PCA) to formulate the tracking problem as one of minimizing differentiable objective functions. Our experiments show that the differential structure of these objective functions is rich enough to take advantage of standard deterministic optimization methods, whose computational requirements are much smaller than those of probabilistic ones and can nevertheless yield very good results even in difficult situations.

We use stereo data acquired using a Digiclops operating at a 640x480 resolution and a 14Hz framerate, which is relatively slow when it comes to capturing a running motion. The quality of the data is poor for several reasons:

  • First, to avoid motion blur, we had to use a high shutter speed that reduces exposure too much.
  • Second, because the camera is fixed and the subject must remain within the capture volume, she appears to be very small at the beginning of the sequence.

As a result the data of Figure 1 is very noisy and lacks both resolution and depth.

Figure 1: Input stereo data. Top row: First image of a synchronized trinocular video sequence at three different times. The 3--D points computed by the Digiclops system are reprojected onto the images. Bottom row: Side views of these 3--D points. Note that they are very noisy and lack depth.

For tracking purposes only a small manual interaction is needed. The global position for the first frame is manually initialized, as the virtual time positions for the first and last frames, interpolating at a constant speed the other frames. Then the global motion is compute for every frame in a recursive way. Optimized values for frame t are the initialization values for frame t+1.

Once the global motion is recovered, two different algorithms have been implemented depending of the type of motion to track.

1. Tracking steady motion

In the first one, the assumption that the movement is steady has been done, and only a set of PCA parameters have been optimized for the whole sequence, since if the motion does not vary, only one set of parameters is necessary to describe a motion. Very satisfactory results are shown in Figure 2 for walking sequence.

Figure 2: Tracking a steady walking.

2. Tracking variable motion

When the style changes, or even the activity, the system is not flexible enough to have good results for the whole sequence. To solve that we have done a new tracker where there is an entire set of PCA parameters for each frame, allowing the system to automatically evolve from one activity to another. This is shown in Figure 5, where the subject starts walking, then for a couple of frames she performs the transition and then runs. Results for a non-steady running are shown in Figure 3, while in Figure 4 for the variable walking.

Figure 3: Tracking a running motion while allowing the style to vary. The legs are correctly positioned in the whole sequence.

Figure 4: Tracking a walking motion while allowing the style to vary.

3. Multi-activity tracking

We can partially overcome one of the major limitations of approaches that rely on motion-models, namely that they limit the algorithms to the particular class of motion from which the models have been created. This is achieved by performing PCA on motion databases that contain multiple classes of motions as opposed to a single one, which yields a decomposition in which the first few components can be used to classify the motion and can evolve during tracking to model the transition from one kind of motion to another.

Figure 4: Tracking the transition between walking and running. In the first four frames the subject is running. The transition occurs in the following three frames and the sequence ends with running.

We show the effectiveness of the proposed approach by using it to fit full-body models to stereo data of people walking and running and whose quality is too low to yield satisfactory results without models. This stereo data simply provides us with a convenient way to show that this approach performs well on real data. However, any motion tracking algorithm that relies on minimizing an objective function is amenable to the treatment we propose.



R. Urtasun, P. Fua
3D Human Body Tracking using Deterministic Motion Models
In European Conference on Computer Vision, Prague, Czech Republic, May 2004

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