N-IPS, suffers from inefficiency problems if the observation density is uninformative and/or uniformly very small except in a small neighborhood of the true state since then particles which are not in this small vicinity of the ``true'' state will all have roughly equal observation likelihoods and the filter becomes effectively decoupled from the observations.
LS-N-IPS was designed to overcome the problem of N-IPS with peaky observation densities, therefore it is natural to consider it in visual tracking problems. LS-N-IPS combines local search with particle filtering and thus can be thought of as a combination of the two main streams of vision based tracking research mentioned above. As a consequence, the algorithm inherits the high precision of local search based object tracking methods and the robustness of the particle filter based methods, even when a small number of particles is used.
Here is an MPEG (0.7Mb) showing the LS-N-IPS algorithm tracking an object, in a cluttered room. Tracking is real time. Only 100 particles were used.
Here is an MPEG (0.5Mb) showing the LS-N-IPS algorithm tracking a hand in real-time, in a dark room with 100 particles.
Here is an MPEG (0.7Mb) showing the LS-N-IPS algorithm tracking a face, in a dark room with 100 particles. Real time performance.
This MPEG (0.9Mb) shows tracking a hand moving in front of the face, in a cluttered room with only 50 particles. 73 frames can be processed per second on a P4, 1.4GHz Pentium. The Local Search is done by an artificial neural network trained in advance.
This MPEG (5.8Mb) shows tracking a facial mask moving in 3D. The Local Search here is a local pose from shading algorithm. The left of the image is the original video, while the right hand side is the tracking result.
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Last updated 1st July, 2002