  Object detection is one of the most active areas in computer vision, which
has made significant improvement in recent years. Current state-of-the-art
object detection methods mostly adhere to the framework of regions with
convolutional neural network (R-CNN) and only use local appearance features
inside object bounding boxes. Since these approaches ignore the contextual
information around the object proposals, the outcome of these detectors may
generate a semantically incoherent interpretation of the input image. In this
paper, we propose an ensemble object detection system which incorporates the
local appearance, the contextual information in term of relationships among
objects and the global scene based contextual feature generated by a
convolutional neural network. The system is formulated as a fully connected
conditional random field (CRF) defined on object proposals and the contextual
constraints among object proposals are modeled as edges naturally. Furthermore,
a fast mean field approximation method is utilized to inference in this CRF
model efficiently. The experimental results demonstrate that our approach
achieves a higher mean average precision (mAP) on PASCAL VOC 2007 datasets
compared to the baseline algorithm Faster R-CNN.
