City18
* *AI City Challenge
* 2018 NVIDIA AI City Challenge, The
* AIC2018 Report: Traffic Surveillance Research
* Challenges on Large Scale Surveillance Video Analysis
* Dual-Mode Vehicle Motion Pattern Learning for High Performance Road Traffic Anomaly Detection
* Geometry-Aware Traffic Flow Analysis by Detection and Tracking
* Graph@FIT Submission to the NVIDIA AI City Challenge 2018
* Semi-Automatic 2D Solution for Vehicle Speed Estimation from Monocular Videos, A
* Single-Camera and Inter-Camera Vehicle Tracking and 3D Speed Estimation Based on Fusion of Visual and Semantic Features
* Speed Estimation and Abnormality Detection from Surveillance Cameras
* Traffic Flow Analysis with Multiple Adaptive Vehicle Detectors and Velocity Estimation with Landmark-Based Scanlines
* Traffic Speed Estimation from Surveillance Video Data: For the 2nd NVIDIA AI City Challenge Track 1
* Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling
* Unsupervised Vehicle Re-identification Using Triplet Networks
* Vehicle Re-identification with the Space-Time Prior
* Vehicle Tracking and Speed Estimation from Traffic Videos
* Video Analytics in Smart Transportation for the AIC'18 Challenge
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City20
* *AI City Challenge
* 4th AI City Challenge, The
* AI City Challenge 2020: Computer Vision for Smart Transportation Applications
* Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification
* City-Scale Multi-Camera Vehicle Tracking by Semantic Attribute Parsing and Cross-Camera Tracklet Matching
* Countor: count without bells and whistles
* Determining Vehicle Turn Counts at Multiple Intersections by Separated Vehicle Classes Using CNNs
* Dual Embedding Expansion for Vehicle Re-identification
* ELECTRICITY: An Efficient Multi-camera Vehicle Tracking System for Intelligent City
* Fast Unsupervised Anomaly Detection in Traffic Videos
* Fractional Data Distillation Model for Anomaly Detection in Traffic Videos
* Further Non-local and Channel Attention Networks for Vehicle Re-identification
* Going Beyond Real Data: A Robust Visual Representation for Vehicle Re-identification
* iTASK - Intelligent Traffic Analysis Software Kit
* Large Scale Vehicle Re-Identification by Knowledge Transfer from Simulated Data and Temporal Attention
* Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
* Multi-Granularity Tracking with Modularlized Components for Unsupervised Vehicles Anomaly Detection
* Robust and Fast Vehicle Turn-counts at Intersections via an Integrated Solution from Detection, Tracking and Trajectory Modeling
* Robust Movement-Specific Vehicle Counting at Crowded Intersections
* StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification
* Towards Real-Time Systems for Vehicle Re-Identification, Multi-Camera Tracking, and Anomaly Detection
* Towards Real-time Traffic Movement Count and Trajectory Reconstruction Using Virtual Traffic Lanes
* Vehicle Counts by Class Framework using Distinguished Regions Tracking at Multiple Intersections, A
* Vehicle Re-Identification Based on Complementary Features
* Vehicle Re-Identification in Multi-Camera scenarios based on Ensembling Deep Learning Features
* Viewpoint-aware Channel-wise Attentive Network for Vehicle Re-identification
* VOC-RelD: Vehicle Re-identification based on Vehicle-Orientation-Camera
* Zero-VIRUS*: Zero-shot Vehicle Route Understanding System for Intelligent Transportation
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CityModels09
* *Workshop on Quality, Scale & Analysis Aspects of City Models
* 3D city GIS for sustaining city infrastructure: the Bentley approach
* 3D city registration and enrichment
* Approximate geometric reasoning with extended geographic objects
* City model and scale model of the city of Zagreb
* Considering uncertainties within an emergency prediction model in an urban environement
* Development of an advanced uncertainty measure for classified remotely sensed scenes
* framework for generalization of city models based on CityGML and X3D, A
* Generalization of 3D City Models as a Service
* Geometric reasoning in 3D building models using multivariate polynomials and characteristic sets
* Integration of rendering technologies and visualization techniques to improve 3D Mobile GIS applications
* Learning semantic models and grammar rules of building parts
* mathematic model for event detection in spatiotemporal city environment, A
* Modeling of urban industrial economy through utilization of thermal band
* Quality assessment of historical Baalbek's 3D city model
* Road traffic noise: GIS tools for noise mapping and a case study for Skåne region
* Semi-automatic City Model Extraction from Tri-stereoscopic VHR Satellite Imagery
* Space allocation of educational centers using multiplicatively weighted Voronoi diagram
* Terrain-dependent aggregation of 3D city models
* Visual Analytics approach for considering uncertainty information
20 for CityModels09