Optical Flow-based Tracking of Vehicles using Adaptive Particle Filter Target Tracking Algorithm based for Accident Prevention

Application of the object tracking algorithm sampling method calva distance scales outlined in the vehicle tracking capture particle filter for performing adaptive sampling of Kullback-Leibler Distance (KLD) based particle liters size to improve efficiency by adapting the method of the statistical sample set during estimation. The key idea is that in the KLD based sampling method, the sample represented an approximation error introduced later particles combination thereof. The name KLD sampling because of measure Cluck, Lebo approximate distance error. Density is focused on a small portion of the state space. If the uncertain state is high, it will choose the adaptation method of a small sample of among many of the sample. The traditional method of Support Vector Machines (SVM) algorithm, random forest, RADAR simulator design, which includes a simple calculation algorithm for modifying the particle filter algorithm and is used to verify the integrity of the particles by weight of radar signal processing. The object tracking algorithm and sampling method outlined in the vehicle tracking capture particle filter is used here to perform adaptive sampling of Kullback-Leibler Distance (KLD) based particle size to improve efficiency by adapting the method of the statistical sample set during estimation. The key idea is that in the KLD based sampling method, the sample represented an approximation error, which introduced later particles combination thereof. Density is concentrated in a small area of the state. If the level of uncertainty is high, it will select the adaptation method of a small sample from a large sample.