CenterRepp: Predict Central Representative Point Set's Distribution For Detection

Object detection has long been an important issue in the discipline of scene understanding. Existing researches mainly focus on the object itself, ignoring its surrounding environment. In fact, the surrounding environment provides abundant information to help detectors classify and locate objects. This paper proposes CRPDet, viz. CenterRepp Detector, a framework for object detection. The main function of CRPDet is accomplished by the CenterRepp module, which takes into account the surrounding environment by predicting the distribution of the central representative points. CenterRepp converts labeled object frames into the mean and standard variance of the sampling points' distribution. This helps increase the receptive field of objects, breaking the limitation of object frames. CenterRepp defines a position-fixed center point with significant weights, avoiding to sample all points in the surroundings. Experiments on the COCO test-dev detection benchmark demonstrates that our proposed CRPDet has comparable performance with state-of-the-art detectors, achieving 39.4 mAP with 51 FPS tested under single size input.

[1]  Hong-Yuan Mark Liao,et al.  YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.

[2]  Vladlen Koltun,et al.  Tracking Objects as Points , 2020, ECCV.

[3]  Larry S. Davis,et al.  SaccadeNet: A Fast and Accurate Object Detector , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Fei Wang,et al.  CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Guanglu Song,et al.  Revisiting the Sibling Head in Object Detector , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Zhiqiang Shen,et al.  Soft Anchor-Point Object Detection , 2019, ECCV.

[7]  Quoc V. Le,et al.  EfficientDet: Scalable and Efficient Object Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jiliang Luo,et al.  CenterFace: Joint Face Detection and Alignment Using Face as Point , 2019, Sci. Program..

[9]  Stephen Lin,et al.  RepPoints: Point Set Representation for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[10]  Jongyoul Park,et al.  An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[11]  Qi Tian,et al.  CenterNet: Keypoint Triplets for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[12]  Xingyi Zhou,et al.  Objects as Points , 2019, ArXiv.

[13]  Zicheng Liu,et al.  Rethinking Classification and Localization in R-CNN , 2019, ArXiv.

[14]  Yuning Jiang,et al.  FoveaBox: Beyond Anchor-based Object Detector , 2019, ArXiv.

[15]  Shengcai Liao,et al.  Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection , 2019, Pattern Recognit..

[16]  Hao Chen,et al.  FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[17]  Marios Savvides,et al.  Feature Selective Anchor-Free Module for Single-Shot Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Philipp Krähenbühl,et al.  Bottom-Up Object Detection by Grouping Extreme and Center Points , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Kai Chen,et al.  Region Proposal by Guided Anchoring , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Zhaoxiang Zhang,et al.  Scale-Aware Trident Networks for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[21]  Stephen Lin,et al.  Deformable ConvNets V2: More Deformable, Better Results , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, International Journal of Computer Vision.

[23]  Yuning Jiang,et al.  Acquisition of Localization Confidence for Accurate Object Detection , 2018, ECCV.

[24]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[26]  Trevor Darrell,et al.  Deep Layer Aggregation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[28]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Yuning Jiang,et al.  UnitBox: An Advanced Object Detection Network , 2016, ACM Multimedia.

[30]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[31]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[32]  Yi Yang,et al.  DenseBox: Unifying Landmark Localization with End to End Object Detection , 2015, ArXiv.

[33]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[35]  Christopher K. I. Williams,et al.  International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge , 2022 .

[36]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.