Question: For a balanced binary training dataset i.e number of data points with class +1 are equal to number of data points with class -1 , what will happen if we use weak binary classifiers whose classification accuracy is less
than 50%, say 30%? Will the performance of the combined classifier improve with more iterations?

I am aware that binary classifiers who perform worse than 50%, still contribute to final prediction, but will the performance of combined classifiers improve with more iterations ?

Answer: 
Semantic segmentation of an image (at least in context of CNN based methods) can simply be viewed as a pixel-wise classification problem, that is to say that you assign a class to every pixel of the image. So in your case if you are able to successfully perform a semantic segmentation of your camera feed then the problem of classification of cars/no cars is trivial or rather non-existent since you were able to solve a more difficult problem of classifying every pixel. Just check for cars in your pixel-wise classification output.

However, if your objective is only to identify whether or not there is a car in frame then honestly semantic segmentation is a overkill and I don't see any reason why you can't train a near perfect binary classifier for cars/no cars problem which would be robust to various background artifacts(buildings etc.) even with limited data. Try transfer learning on ILSVRC pre-trained resnet 34 or smaller variants. Fine tune the final few layers first and then when you hit saturation fine tune the entire network with these updated weights. You can also try cyclical learning rates (<https://arxiv.org/abs/1506.01186>).
Yes, having ground truth segmentation would make classification tasks quite easy, perhaps even trivial. 

A small nitpick is that a semantic segmentation map is not an image "colored according to type". The colored image is merely a visualization of the $K$ channel segmentation map, which when $K > 3$, cannot be directly visualized.