  Vision-based dynamic pedestrian intrusion detection (PID), judging whether
pedestrians intrude an area-of-interest (AoI) by a moving camera, is an
important task in mobile surveillance. The dynamically changing AoIs and a
number of pedestrians in video frames increase the difficulty and computational
complexity of determining whether pedestrians intrude the AoI, which makes
previous algorithms incapable of this task. In this paper, we propose a novel
and efficient multi-task deep neural network, PIDNet, to solve this problem.
PIDNet is mainly designed by considering two factors: accurately segmenting the
dynamically changing AoIs from a video frame captured by the moving camera and
quickly detecting pedestrians from the generated AoI-contained areas. Three
efficient network designs are proposed and incorporated into PIDNet to reduce
the computational complexity: 1) a special PID task backbone for feature
sharing, 2) a feature cropping module for feature cropping, and 3) a lighter
detection branch network for feature compression. In addition, considering
there are no public datasets and benchmarks in this field, we establish a
benchmark dataset to evaluate the proposed network and give the corresponding
evaluation metrics for the first time. Experimental results show that PIDNet
can achieve 67.1% PID accuracy and 9.6 fps inference speed on the proposed
dataset, which serves as a good baseline for the future vision-based dynamic
PID study.
