  With the growing advances in deep learning based technologies the detection
and identification of co-occurring objects is a challenging task which has many
applications in areas such as, security and surveillance. In this paper, we
propose a novel framework called SDLNet- Statistical analysis with Deep
Learning Network that identifies co-occurring objects in conjunction with base
objects in multilabel object categories. The pipeline of proposed work is
implemented in two stages: in the first stage of SDLNet we deal with multilabel
detectors for discovering labels, and in the second stage we perform
co-occurrence matrix analysis. In co-occurrence matrix analysis, we learn
co-occurrence statistics by setting base classes and frequently occurring
classes, following this we build association rules and generate frequent
patterns. The crucial part of SDLNet is recognizing base classes and making
consideration for co-occurring classes. Finally, the generated co-occurrence
matrix based on frequent patterns will show base classes and their
corresponding co-occurring classes. SDLNet is evaluated on two publicly
available datasets: Pascal VOC and MS-COCO. The experimental results on these
benchmark datasets are reported in Sec 4.
