UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring

The rapid advances of transportation infrastructure have led to a dramatic increase in the demand for smart systems capable of monitoring traffic and street safety. Fundamental to these applications are a community-based evaluation platform and benchmark for object detection and multi-object tracking. To this end, we organize the AVSS2017 Challenge on Advanced Traffic Monitoring, in conjunction with the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), to evaluate the state-of-the-art object detection and multi-object tracking algorithms in the relevance of traffic surveillance. Submitted algorithms are evaluated using the large-scale UA-DETRAC benchmark and evaluation protocol. The benchmark, the evaluation toolkit and the algorithm performance are publicly available from the website http://detrac-db.rit.albany.edu.

[1]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[2]  Charless C. Fowlkes,et al.  Globally-optimal greedy algorithms for tracking a variable number of objects , 2011, CVPR 2011.

[3]  Hong Wang,et al.  Evolving boxes for fast vehicle detection , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[4]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Stefan Roth,et al.  MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking , 2015, ArXiv.

[6]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Ming-Hsuan Yang,et al.  DETRAC: A New Benchmark and Protocol for Multi-Object Tracking , 2015, ArXiv.

[9]  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.

[10]  Konrad Schindler,et al.  Discrete-continuous optimization for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Volker Eiselein,et al.  Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Nuno Vasconcelos,et al.  Learning Complexity-Aware Cascades for Deep Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Stefan Roth,et al.  MOT16: A Benchmark for Multi-Object Tracking , 2016, ArXiv.

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Konrad Schindler,et al.  Multi-target tracking by continuous energy minimization , 2011, CVPR 2011.

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

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

[20]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Rainer Stiefelhagen,et al.  Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..

[22]  J. Munkres ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[23]  Namil Kim,et al.  Multispectral pedestrian detection: Benchmark dataset and baseline , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Sergio Guadarrama,et al.  Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  J. Ferryman,et al.  PETS2009: Dataset and challenge , 2009, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance.

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

[27]  Dietrich Paulus,et al.  Simple online and realtime tracking with a deep association metric , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[28]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[29]  Luc Van Gool,et al.  Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[30]  Hanan Samet,et al.  Pruning Filters for Efficient ConvNets , 2016, ICLR.

[31]  Mario Sznaier,et al.  The Way They Move: Tracking Multiple Targets with Similar Appearance , 2013, 2013 IEEE International Conference on Computer Vision.

[32]  Junjie Yan,et al.  Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[34]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Kuk-Jin Yoon,et al.  Robust Online Multi-object Tracking Based on Tracklet Confidence and Online Discriminative Appearance Learning , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.