  This paper demonstrates for the first time that a biologically-plausible
spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity
(STDP) can continuously learn to detect walking people on the fly using
retina-inspired, event-based cameras. Our pipeline works as follows. First, a
short sequence of event data ($<2$ minutes), capturing a walking human by a
flying drone, is forwarded to a convolutional SNNSTDP system which also
receives teacher spiking signals from a readout (forming a semi-supervised
system). Then, STDP adaptation is stopped and the learned system is assessed on
testing sequences. We conduct several experiments to study the effect of key
parameters in our system and to compare it against conventionally-trained CNNs.
We show that our system reaches a higher peak $F_1$ score (+19%) compared to
CNNs with event-based camera frames, while enabling on-line adaptation.
