A multi-target corner pooling-based neural network for vehicle detection

Convolutional neural network has shown strong capability to improve performance in vehicle detection, which is one of the main research topics of intelligent transportation system. Aiming to detect the blocked vehicles efficiently in actual traffic scenes, we propose a novel convolutional neural network based on multi-target corner pooling layers. The hourglass network, which could extract local and global information of the vehicles in the images simultaneously, is chosen as the backbone network to provide vehicles’ features. Instead of using the max pooling layer, the proposed multi-target corner pooling (MTCP) layer is used to generate the vehicles’ corners. And in order to complete the blocked corners that cannot be generated by MTCP, a novel matching corners method is adopted in the network. Therefore, the proposed network can detect blocked vehicles accurately. Experiments demonstrate that the proposed network achieves an AP of 43.5% on MS COCO dataset and a precision of 93.6% on traffic videos, which outperforms the several existing detectors.

[1]  Hanqing Lu,et al.  CoupleNet: Coupling Global Structure with Local Parts for Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[2]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[4]  Rong Chen,et al.  The Influence Ranking for Testers in Bug Tracking Systems , 2019, Int. J. Softw. Eng. Knowl. Eng..

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  David Gómez-Gutiérrez,et al.  Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System , 2018, Sensors.

[7]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[8]  Lars Petersson,et al.  DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[9]  Fei-Yue Wang.  Scanning the Issue and Beyond: Crowdsourcing for Field Transportation Studies and Services , 2015, IEEE Trans. Intell. Transp. Syst..

[10]  Fei-Yue Wang,et al.  Vehicle detection grammars with partial occlusion handling for traffic surveillance , 2015 .

[11]  Larry S. Davis,et al.  Soft-NMS — Improving Object Detection with One Line of Code , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[12]  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.

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

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

[15]  Shiming Xiang,et al.  Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks , 2014, IEEE Geoscience and Remote Sensing Letters.

[16]  Dong Xu,et al.  Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey , 2018, IEEE Signal Processing Magazine.

[17]  Ming Tang,et al.  Hierarchical and Networked Vehicle Surveillance in ITS: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.

[18]  Shiru Qu,et al.  Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition , 2017 .

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

[20]  Oihana Otaegui,et al.  Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification , 2012, IEEE Transactions on Intelligent Transportation Systems.

[21]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Zhiqiang Shen,et al.  DSOD: Learning Deeply Supervised Object Detectors from Scratch , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[23]  Jian Sun,et al.  Object Detection Networks on Convolutional Feature Maps , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[25]  Shifeng Zhang,et al.  Single-Shot Refinement Neural Network for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[26]  Wei Liu,et al.  DSSD : Deconvolutional Single Shot Detector , 2017, ArXiv.

[27]  Inbum Jung,et al.  Analysis of Vehicle Detection with WSN-Based Ultrasonic Sensors , 2014, Sensors.

[28]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Yang Gao,et al.  Scale optimization for full-image-CNN vehicle detection , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[30]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[32]  Kyoung Ho Choi,et al.  Performance of vehicle speed estimation using wireless sensor networks: a region-based approach , 2014, The Journal of Supercomputing.

[33]  M. M. Naushad Ali,et al.  Multiple object tracking with partial occlusion handling using salient feature points , 2014, Inf. Sci..

[34]  Zhiao Huang,et al.  Associative Embedding: End-to-End Learning for Joint Detection and Grouping , 2016, NIPS.