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Given the current state of a vehicle at image frame ( t ), we use Kalman filtering to predict the state of a vehicle in the next image frame ( t + 1 ) [41].
Finally, the K distributions are sorted in weights for matching operations in the next image frame ( t + 1 ).
The feature-spatial model of a vehicle is characterized in image frame ( t ) and predicted for comparison against the target in frame ( t + 1 ).
At each image frame ( t ), for each pixel, the new observation of the pixel is matched against each Gaussian distribution of that pixel.
In either case, the best-matched distribution is updated by increasing its weight and "learning" its mean and variance for the next image frame ( t + 1 ) as follows [25]: left.
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After the initialization, a temporary sprite of stationary scene, denoted as T C SS and T M SS, is obtained between each input image frame I t and its previous frame I t-1 to extract the useful information of occluded background in I t.
Then an arbiter with threshold A is used to divide the pixels of input image frame I t into stationary part I s and rest part I r.
where I x, y, t) image for frame t from total N.
Given N binary gait silhouette image frames, B t (x,y), the gray-level GEI is defined as: G x,y)=frac{1}{N}sum_{t=1}^{N} B_{t} x,y).
After applying the RPHC algorithm to the image in the first frame t = 1, the trackers were placed at the positions Ψ 1 = (ψ 1, 1, …, ψ 1, g ) of the g non-overlapping objects in which redundant positions were removed from the m points of convergence.
We obtained a time series of 512 × 256 × 20 images for each time frame t ∈ { 1, …, T }, where T = 500.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com