26 research outputs found
F2DNet: Fast Focal Detection Network for Pedestrian Detection
Two-stage detectors are state-of-the-art in object detection as well as
pedestrian detection. However, the current two-stage detectors are inefficient
as they do bounding box regression in multiple steps i.e. in region proposal
networks and bounding box heads. Also, the anchor-based region proposal
networks are computationally expensive to train. We propose F2DNet, a novel
two-stage detection architecture which eliminates redundancy of current
two-stage detectors by replacing the region proposal network with our focal
detection network and bounding box head with our fast suppression head. We
benchmark F2DNet on top pedestrian detection datasets, thoroughly compare it
against the existing state-of-the-art detectors and conduct cross dataset
evaluation to test the generalizability of our model to unseen data. Our F2DNet
achieves 8.7\%, 2.2\%, and 6.1\% MR-2 on City Persons, Caltech Pedestrian, and
Euro City Person datasets respectively when trained on a single dataset and
reaches 20.4\% and 26.2\% MR-2 in heavy occlusion setting of Caltech Pedestrian
and City Persons datasets when using progressive fine-tunning. Furthermore,
F2DNet have significantly lesser inference time compared to the current
state-of-the-art. Code and trained models will be available at
https://github.com/AbdulHannanKhan/F2DNet.Comment: Accepted at ICPR 202
PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search
Person search is a challenging problem with various real-world applications,
that aims at joint person detection and re-identification of a query person
from uncropped gallery images. Although, the previous study focuses on rich
feature information learning, it is still hard to retrieve the query person due
to the occurrence of appearance deformations and background distractors. In
this paper, we propose a novel attention-aware relation mixer (ARM) module for
person search, which exploits the global relation between different local
regions within RoI of a person and make it robust against various appearance
deformations and occlusion. The proposed ARM is composed of a relation mixer
block and a spatio-channel attention layer. The relation mixer block introduces
a spatially attended spatial mixing and a channel-wise attended channel mixing
for effectively capturing discriminative relation features within an RoI. These
discriminative relation features are further enriched by introducing a
spatio-channel attention where the foreground and background discriminability
is empowered in a joint spatio-channel space. Our ARM module is generic and it
does not rely on fine-grained supervision or topological assumptions, hence
being easily integrated into any Faster R-CNN based person search methods.
Comprehensive experiments are performed on two challenging benchmark datasets:
CUHKSYSU and PRW. Our PS-ARM achieves state-of-the-art performance on both
datasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gain
of 5 in the mAP score over SeqNet, while operating at a comparable speed.Comment: Paper accepted in ACCV 202