In regards to the classification, I was thinking of the possible problems I might run into with my idea. My idea is to have a human label the correct incoming traffic object in the training set. The system will save these object's trajectories. For testing, when given a video footage(but in the same location) the system has not seen before, it will do the blob detection and check the current trajectory if it near matches the ones in the training set.
The problems I see are:
- occlusion will happen sometimes so the incoming traffic will might only have half the trajectory.
- a vehicle traveling north in the opposing lane will have the close to the same points as the incoming traffic. --> need to use time/frame numbers to help.
On a side note, check out the power of human low-resolution recognition
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