Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd   Pedestrian detection in crowded scenes is a challenging problem since the
pedestrians often gather together and occlude each other. In this paper, we
propose a new occlusion-aware R-CNN (OR-CNN) to improve the detection accuracy
in the crowd. Specifically, we design a new aggregation loss to enforce
proposals to be close and locate compactly to the corresponding objects.
Meanwhile, we use a new part occlusion-aware region of interest (PORoI) pooling
unit to replace the RoI pooling layer in order to integrate the prior structure
information of human body with visibility prediction into the network to handle
occlusion. Our detector is trained in an end-to-end fashion, which achieves
state-of-the-art results on three pedestrian detection datasets, i.e.,
CityPersons, ETH, and INRIA, and performs on-pair with the state-of-the-arts on
Caltech.

35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ