A One-and-Half Stage Pedestrian Detector.

Ujjwal, A. Dziri, B. Leroy, F. Bremond

Abstract:

edestrian detection is a specific instance of the more
general problem of object detection in computer vision. A
balance between detection accuracy and speed is a desir-
able trait for pedestrian detection systems in many appli-
cations such as self-driving cars. In this paper, we follow
the wisdom of “ and less is often more” to achieve this
balance. We propose a lightweight mechanism based on
semantic segmentation to reduce the number of anchors to
be processed. We furthermore unify this selection with the
intra-anchor feature pooling strategy adopted in high per-
formance two-stage detectors such as Faster-RCNN. Such a
strategy is avoided in one-stage detectors like SSD in favour
of faster inference but at the cost of reducing the accuracy
vis-a-vis two-stage detectors. However our anchor selec-
tion renders it practical to use feature pooling without giv-
ing up the inference speed.
Our proposed approach succeeds in detecting pedes-
trians with state-of-art performance on caltech-reasonable
and ciypersons datasets with inference speeds of ∼32fps.