Object Detection via End to End Integration of Aspect Ratio and Context Aware Part based Models and Fully Convolutional Networks
This paper presents a framework of integrating a mixture of part based models and region based convolutional networks for accurate and efficient object detection. Each mixture component consists of a small number of parts accounting for both object aspect ratio and contextual information explicitly. The mixture is category agnostic for the simplicity of scaling up in applications. Both object aspect ratio and context have been extensively studied in traditional object detection systems such as the mixture of deformable part based models [13] They are, however, largely ignored in deep neural network based detection systems[17, 16, 39, 8] The proposed method addresses this issue in two fold: (i) It remedies the wrapping artifact due to the generic RoI (region of interest) pooling (e.g. a 3x 3 grid) by taking into account object aspect ratios. (ii) It models both global (from the whole image) and local (from the surrounding of a bounding box) context for improving performance. The integrated framework is fully convolutional and enjoys end to end training, which we call the aspect ratio and context aware fully convolutional network (ARCFCN) In experiments, ARC FCN shows very competitive results on the PASCAL VOC datasets, especially, it outperforms both Faster R CNN [39] and R FCN [8] with significantly better mean average precision (mAP) using larger value for the intersection over union (IoU) threshold (i.e. 0.7 in the experiments) ARC FCN is still sufficiently efficient with a test time speed of 380ms per image, faster than the Faster R CNN but slower than the R FCN. *Tianfu Wu is the corresponding author. +This work was done when Shuai Shao was an Intern at YunOS BU, Alibaba Group. Feature Warp RoI Pooling Fast /Faster RCNN R CNN
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