Workshop Program
AI City Challenge CVPR Workshop 2019
- Sunday, June 16, 2019
- Long Beach, CA
- Long Beach Convention Center - Room 101B
09:30 – 10:10
Two Oral Presentations (20 minutes each)
Shuai Bai; Zhiqun He; Yu Lei; Wei Wu; Chengkai Zhu; Ming Sun; Junjie Yan
Gaoang Wang; Xinyu Yuan; Aotian Zheng; Hung-Min Hsu; Jenq-Neng Hwang
10:10 – 10:30
Coffee Break
10:30 – 12:10
Five Oral Presentations (20 minutes each)
(3) AI City Challenge 2019 – City-scale Video Analytics for Smart Transportation
Ming-Ching Chang; Jiayi Wei; Zheng-An Zhu; Yan-Ming Chen; Chan-Shuo Hu; Ming-Xiu Jiang; Chen-Kuo Chiang
(4) Unsupervised Traffic Anomaly Detection Using Trajectories
Jianfei Zhao; Zitong Yi; Siyang Pan; Yanyun Zhao; Zhicheng Zhao; Fei Su; Bojin Zhuang
(5) Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-adaptive Vehicle Detectors for Traffic Video Analysis
(5) Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-adaptive Vehicle Detectors for Traffic Video Analysis
Khac-Tuan Nguyen; Trung-Hieu Hoang; Minh-Triet Tran; Trung-Nghia Le; Ngoc-Minh Bui; Trong-Le Do; Viet-Khoa Vo-Ho; Quoc-An Luong; Mai-Khiem Tran; Thanh-An Nguyen; Thanh-Dat Truong; Vinh-Tiep Nguyen; Minh Do
(6) A Comparative Study of Faster R-CNN Models for Anomaly Detection in 2019 AI City Challenge
(6) A Comparative Study of Faster R-CNN Models for Anomaly Detection in 2019 AI City Challenge
Linu Shine; Anitha Edison; Jiji C.V.
(7) Attention Driven Vehicle Re-identification and Unsupervised Anomaly Detection for Traffic Understanding
(7) Attention Driven Vehicle Re-identification and Unsupervised Anomaly Detection for Traffic Understanding
Pirazh Khorramshahi; Neehar Peri; Amit Kumar; Anshul Shah; Rama Chellappa
12:10 – 13:40
Lunch
13:40 – 15:20
Five Oral Presentations (20 minutes each)
(8) Multi-camera Vehicle Tracking and Re-identification Based on Visual and Spatial-temporal Features
Xiao Tan; Zhigang Wang; Minyue Jiang; Xipeng Yang; Jian Wang; Yuan Gao; Xiangbo Su; Xiaoqing Ye; Yuchen Yuan; Dongliang He; Shilei Wen; Errui Ding
(9) Multi-view Vehicle Re-identification Using Temporal Attention Model and Metadata Re-ranking
(9) Multi-view Vehicle Re-identification Using Temporal Attention Model and Metadata Re-ranking
Tsung-Wei Huang; Jiarui Cai; Hao Yang; Hung-Min Hsu; Jenq-Neng Hwang
(10) Partition and Reunion: A Two-branch Neural Network for Vehicle Re-identification
(10) Partition and Reunion: A Two-branch Neural Network for Vehicle Re-identification
Hao Chen; Benoit Lagadec; Francois Bremond
(11) Multi-camera Vehicle Tracking with Powerful Visual Features and Spatial-temporal Cue
(11) Multi-camera Vehicle Tracking with Powerful Visual Features and Spatial-temporal Cue
Zhiqun He; Yu Lei; Shuai Bai; Wei Wu
(12) Vehicle Re-identifiation and Multi-camera Tracking in Challenging City-scale Environment
(12) Vehicle Re-identifiation and Multi-camera Tracking in Challenging City-scale Environment
Jakub Špaňhel; Vojtěch Bartl; Roman Juránek; Adam Herout
15:20 – 15:50
Coffee Break
15:50 – 16:30
Two Oral Presentations (20 minutes each)
Hung-Min Hsu; Tsung-Wei Huang; Gaoang Wang; Jiarui Cai; Zhichao Lei; Jenq-Neng Hwang
(14) A Locality Aware City-scale Multi-Camera Vehicle Tracking System
(14) A Locality Aware City-scale Multi-Camera Vehicle Tracking System
Yunzhong Hou; Heming Du; Liang Zheng
16:30 – 17:00
Announcement of Challenge Winners and Awards Ceremony
17:00 – 18:00
Poster/Demo Session
- The 2019 AI City Challenge
- Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos
- Multi-View Vehicle Re-Identification using Temporal Attention Model and Metadata Re-ranking
- Multi-Camera Tracking of Vehicles based on Deep Features Re-ID and Trajectory-Based Camera Link Models
- Vehicle Re-identification with Location and Time Stamps
- Anomaly Candidate Identification and Starting Time Estimation of Vehicles from Traffic Videos
- A Comparative Study of Faster R-CNN Models for Anomaly Detection in 2019 AI City Challenge
- Spatio-temporal Consistency and Hierarchical Matching for MTMC Vehicle Tracking
- Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification
- A Locality-Aware City-Scale Multi-Camera Vehicle Tracking System
- Deep Feature Fusion with Multiple Granularity for Vehicle Re-identification
- VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification
- VPULab participation at AI City Challenge 2019
18:00
Adjourn
Photo Gallery
