A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and
steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles
efficiently, we first use Sobel algorithm to extract the profile of the object. Then, we employ a new Local Optimum
algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main
advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more
attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones, in
addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs.
Otherwise, the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold
choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are
expressed by a metric derived from the Bhattacharyya coefficient. Here, we use both the counter cue to select the particles
and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by
real visual tracking experiments. Results from simulations and experiments with real video data show the improved
performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance
is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.