  Deep Learning-based object detectors can enhance the capabilities of smart
camera systems in a wide spectrum of machine vision applications including
video surveillance, autonomous driving, robots and drones, smart factory, and
health monitoring. Pedestrian detection plays a key role in all these
applications and deep learning can be used to construct accurate
state-of-the-art detectors. However, such complex paradigms do not scale easily
and are not traditionally implemented in resource-constrained smart cameras for
on-device processing which offers significant advantages in situations when
real-time monitoring and robustness are vital. Efficient neural networks can
not only enable mobile applications and on-device experiences but can also be a
key enabler of privacy and security allowing a user to gain the benefits of
neural networks without needing to send their data to the server to be
evaluated. This work addresses the challenge of achieving a good trade-off
between accuracy and speed for efficient deployment of deep-learning-based
pedestrian detection in smart camera applications. A computationally efficient
architecture is introduced based on separable convolutions and proposes
integrating dense connections across layers and multi-scale feature fusion to
improve representational capacity while decreasing the number of parameters and
operations. In particular, the contributions of this work are the following: 1)
An efficient backbone combining multi-scale feature operations, 2) a more
elaborate loss function for improved localization, 3) an anchor-less approach
for detection, The proposed approach called YOLOpeds is evaluated using the
PETS2009 surveillance dataset on 320x320 images. Overall, YOLOpeds provides
real-time sustained operation of over 30 frames per second with detection rates
in the range of 86% outperforming existing deep learning models.
