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Basically the latentSVM algorithm is an implementation of the latentSVM algorithm of Pedro Felzenswalb. It is one of the more earlier implementations, leading indeed to an operating speed of like 5-10 seconds per image for processing.
You could try to reduce the size of the image and apply a mask that defines regions where people can actually occur (cut out air and buildings for example). You could even use background subtraction to first define moving regions in subsequent images.
However, if you want a faster implementation of this algorithm, go to his website and get the release 5, which uses grammar models and which is way faster in processing.
http://people.cs.uchicago.edu/~rbg/latent/
If you want to go for realtime processing, you will need to implement the algorithm on GPU, or trying to convert it to multicore usage. Also trying other techniques which are faster, like random forests, could be a way to go.
