Pedestrian detection forms a key prob-
lem in computer vision, with applica-
tions that can greatly affect every day
life. In this project, failures in pedes-
trian detectors are attempted to be re-
fined by re-evaluating their results via
a fully convolutional neural network.
The network is trained on a number of
datasets which include a custom par-
tial occluded pedestrian dataset.
The networks efficiency was evaluated
by examining the loss through train-
ing (iterations) and by measuring the
mean intersection union across classes.
As a detector, accuracy was measured
by comparing the network given a
detectors result against state-of-the-art
but also by measuring the networks re-
fined result against the detector with-
out the network.
It was found that although results
vary, the proposed network shows
promising results.