The 2019 AI City Challenge
暂无分享,去创建一个
Jenq-Neng Hwang | Rama Chellappa | David C. Anastasiu | Siwei Lyu | Pranamesh Chakraborty | Ming-Ching Chang | Anuj Sharma | Tingting Huang | Shuo Wang | Zheng Tang | Milind Naphade | R. Chellappa | Siwei Lyu | Jenq-Neng Hwang | Ming-Ching Chang | M. Naphade | Anuj Sharma | D. Anastasiu | Pranamesh Chakraborty | Tingting Huang | Shuo Wang | Zheng Tang
[1] Yifei Zhang,et al. Spatio-temporal Consistency and Hierarchical Matching for Multi-Target Multi-Camera Vehicle Tracking , 2019, CVPR Workshops.
[2] Liang Zheng,et al. A Locality Aware City-Scale Multi-Camera Vehicle Tracking System , 2019, CVPR Workshops.
[3] Shao-Yi Chien,et al. Supervised Joint Domain Learning for Vehicle Re-Identification , 2019, CVPR Workshops.
[4] Murari Mandal,et al. Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos , 2019, CVPR Workshops.
[5] Jenq-Neng Hwang,et al. Camera self-calibration from tracking of moving persons , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[6] Jenq-Neng Hwang,et al. Multi-View Vehicle Re-Identification using Temporal Attention Model and Metadata Re-ranking , 2019, CVPR Workshops.
[7] Jenq-Neng Hwang,et al. Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models , 2019, CVPR Workshops.
[8] Jenq-Neng Hwang,et al. Online-Learning-Based Human Tracking Across Non-Overlapping Cameras , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[9] Ling Xie,et al. Multiview Vehicle Tracking by Graph Matching Model , 2019, CVPR Workshops.
[10] Jenq-Neng Hwang,et al. CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Ram Nevatia,et al. Learning to associate: HybridBoosted multi-target tracker for crowded scene , 2009, CVPR.
[12] Jenq-Neng Hwang,et al. The 2018 NVIDIA AI City Challenge , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[13] Jenq-Neng Hwang,et al. MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D , 2019, IEEE Access.
[14] Abner Ayala-Acevedo,et al. Vehicle Re-Identification: Pushing the limits of re-identification , 2019, CVPR Workshops.
[15] Vinay Sudhakaran,et al. Comparative Study on various Losses for Vehicle Re-identification , 2019, CVPR Workshops.
[16] Francois Bremond,et al. Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification , 2019, CVPR Workshops.
[17] Francesco Solera,et al. Performance Measures and a Data Set for Multi-target, Multi-camera Tracking , 2016, ECCV Workshops.
[18] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[19] David C. Anastasiu,et al. The NVIDIA AI City Challenge , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).
[20] Linu Shine,et al. A Comparative Study of Faster R-CNN Models for Anomaly Detection in 2019 AI City Challenge , 2019, CVPR Workshops.
[21] Wei Wu,et al. Multi-Camera Vehicle Tracking with Powerful Visual Features and Spatial-Temporal Cue , 2019, CVPR Workshops.
[22] Ming-Ching Chang,et al. AI City Challenge 2019 - City-Scale Video Analytics for Smart Transportation , 2019, CVPR Workshops.
[23] Adam Herout,et al. Vehicle Re-Identifiation and Multi-Camera Tracking in Challenging City-Scale Environment , 2019, CVPR Workshops.
[24] Rainer Stiefelhagen,et al. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics , 2008, EURASIP J. Image Video Process..
[25] Farzin Aghdasi,et al. Vehicle Re-identification: an Efficient Baseline Using Triplet Embedding , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[26] Fei Su,et al. Unsupervised Traffic Anomaly Detection Using Trajectories , 2019, CVPR Workshops.
[27] Yi Yang,et al. VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification , 2019, CVPR Workshops.
[28] Yingli Tian,et al. Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019 , 2019, CVPR Workshops.
[29] Xiao Tan,et al. Multi-camera vehicle tracking and re-identification based on visual and spatial-temporal features , 2019, CVPR Workshops.
[30] Zongyao He,et al. Deep Feature Fusion with Multiple Granularity for Vehicle Re-identification , 2019, CVPR Workshops.
[31] Jenq-Neng Hwang,et al. Anomaly Candidate Identification and Starting Time Estimation of Vehicles from Traffic Videos , 2019, CVPR Workshops.
[32] Shaogang Gong,et al. Multi-Task Mutual Learning for Vehicle Re-Identification , 2019, CVPR Workshops.
[33] Jenq-Neng Hwang,et al. ESTHER: Joint Camera Self-Calibration and Automatic Radial Distortion Correction From Tracking of Walking Humans , 2019, IEEE Access.
[34] Wei Wu,et al. Traffic Anomaly Detection via Perspective Map based on Spatial-temporal Information Matrix , 2019, CVPR Workshops.
[35] Rama Chellappa,et al. Attention Driven Vehicle Re-identification and Unsupervised Anomaly Detection for Traffic Understanding , 2019, CVPR Workshops.
[36] Minh N. Do,et al. Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-Adaptive Vehicle Detectors for Traffic Video Analysis , 2019, CVPR Workshops.
[37] Jenq-Neng Hwang,et al. Single-Camera and Inter-Camera Vehicle Tracking and 3D Speed Estimation Based on Fusion of Visual and Semantic Features , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).