Object detection thesis

Ahead of time, we can compute B ( S ) {\displaystyle B(S)} and B ( T ) {\displaystyle B(T)} . Clearly, if these two spheres do not intersect (and that is very easy to test), then neither do S {\displaystyle S} and T {\displaystyle T} . This is not much better than an n -body pruning algorithm, however.

As shown in figure 3-6 & 3-7, we have calculated mean image from last 5 & 15 frames and in order to obtain mask we have subtracted mean image from ith image. we have consider T = 8 for both case. It is easy to implement and adaptive for background calculation. But accuracy is depends on object speed, also memory requirement is very high and have global threshold . same threshold for every pixel. Also from results we can comment that if training signal is too long than memory requirement is very high and if to short than updating rate gets very high. So, slow moving object is not detected property.

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Object detection thesis

object detection thesis


object detection thesisobject detection thesisobject detection thesisobject detection thesis