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The novelty of these papers lies in the use of appearance models to detect limbs in each scene. As the models must be learned online, however, prior knowledge of the position of the limbs must be known before a color-histogram model can be used to represent them. The authors choice of convolution with Harr-like parallel-contrast bars presents a successful approach, but I wonder if this could be highly confounded given backgrounds with stalky objects like trees. An interesting extension would be to apply a similar approach to a person tracker using frame-by-frame motion dynamics. In this case, the appearance template could be adaptively updated at each current tracking position to bootstrap the motion cue. In addition to using the high information content from dynamic motion, this would allow the system to better handle dramatic global appearance changes that occur gradually over a sequence of frames. Overall, I feel the work is a great contribution, and like everyone else, I'm curious about the computational requirements for each frame.

An interesting combination of numerous techniques.

But, can this run in real-time?

Edited by author 09-27-2005 05:12 AM
In regard to possible lighting change problems discussed below, I wonder how much Color-Constancy algorithms would help. I havn't seen seen them used much in the litterature I've read so far, are they all that useful?

 

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So the thresholds for the segment detectors are apparently tweaked for each application (according to the first paper). For example, in the ‘lateral pose’ detector, they choose a threshold that specifically ignores certain arm positions to reduce false positives. This would clearly not work in, say, a ‘frontal pose’ scheme where arms tend to fall in specifically those regions they ignore. There may be some other easily detectable pose, other than their favorite walking pose, which they could detect, but how might they do so if they have to change these thresholds for every possible pose?

Also, I’m worried that lighting changes might pose a significant problem for the tracker.

I'd be interested to know if any work is being done to make this a real time tracker that could be used for some sort of surveillence. It seems not highly likely as there is no mention of track memory.

I'm also curious if an adaptive pose model can be employed to improve background noise.

My biggest concern is how this performs against other trackers that do something such as background subtraction. There is mention that this is robust to non-moving subjects and even moving background which sounds quite desirable but there doesn't seem to be so much advantage to create this pose model (at least in the demonstrations).

I really like the general idea of keying into something (a pose, e.g.) that you can identify with good certainty and combining that with probabilistic tracking.

I think there's something missing in their discussions, though.

They mention that you can't rely on color contrast in real life. But film pros manipulate much more than that. In sports and movie shots, video artists frame the subject, minimize motion blur, control focus and depth of field, and select camera angles to best display the subject's actions.

Many frames in Lola Rennt consist of exactly the canonical poses they're keying off of. But that's clearly a planned, artistic effect in that movie.

The park sequence uses an unmanned camera. For this to work at all, the unmanned camera must use a wide enough field of view to "catch" people in the right pose. The aperture and shutter speed must be set to cover a range of distances without introducing too much motion blur. Of necessity, subjects are smaller and less crisply delineated, and the subject lighting more variable with the unmanned setup. They mention do that this sequence is harder to work with, listing some of these qualities. But they don't then go on to connect those differences to the human-control element in the professional footage and discuss how much that human, artistic element is actually responsible for creating tracking qualities that are well-suited to their method.