Research Article
Multitarget Tracking of Pedestrians in Video Sequences Based on Particle Filters
Algorithm 1
Our algorithm.
(1) Extract target regions in the first frames | (1.1) Perform background subtraction and get motion region. | (1.2) Building a pedestrian detector using HOG descriptors and SVM. | (1.3) Detect pedestrians in each frame. | (2) Build up the discriminative appearance model | For | (2.1) Randomly sample patches | Generate superpixel patches and LBP patches for each target. | Extract superpixel descriptor and LBP descriptor for all patches. | End For | For target | (2.2) Construct Codebook | perform meanshift clustering for all superpixel descriptor, cluster centers | compose the superpixel codebook. | perform meanshift clustering for all LBP descriptor, cluster centers compose | the LBP codebook. | For | Find its nearest keyword and make statistics of the appearance times of the keyword. | End For | Compose trained bags . | End For | (3) Tracking | For target | (3.1) Initialize particle state distribution using the center of specifying region. | (3.2) Set initial weight value of feature information a = b = 0. | End For | For | For target | (3.3) Important sampling step | Propagate and get new particles using (7). | (3.4) Update the weights | Compute the observation likelihood function and for each | particle using (11). | Update weights value of features information using (12). | If | | End if | End For | End For | (3.5) Update codebook | For each frames | Perform mean shift clustering again on and the old codebook using (3). | End For | (3.6) State estimation | Estimate the state |
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