  Detection of pedestrians on embedded devices, such as those on-board of
robots and drones, has many applications including road intersection
monitoring, security, crowd monitoring and surveillance, to name a few.
However, the problem can be challenging due to continuously-changing camera
viewpoint and varying object appearances as well as the need for lightweight
algorithms suitable for embedded systems. This paper proposes a robust
framework for pedestrian detection in many footages. The framework performs
fine and coarse detections on different image regions and exploits temporal and
spatial characteristics to attain enhanced accuracy and real time performance
on embedded boards. The framework uses the Yolo-v3 object detection [1] as its
backbone detector and runs on the Nvidia Jetson TX2 embedded board, however
other detectors and/or boards can be used as well. The performance of the
framework is demonstrated on two established datasets and its achievement of
the second place in CVPR 2019 Embedded Real-Time Inference (ERTI) Challenge.
