Enriched and discriminative convolutional neural network features for pedestrian re‐identification and trajectory modeling
Open Access
- 25 August 2021
- journal article
- research article
- Published by Elsevier in Computer-Aided Civil and Infrastructure Engineering
- Vol. 37 (5) , 573-592
Abstract
No abstract availableKeywords
This publication has 49 references indexed in Scilit:
- A large-scale controlled experiment on pedestrian walking behavior involving individuals with disabilitiesTravel Behaviour and Society, 2017
- Deep Learning‐Based Crack Damage Detection Using Convolutional Neural NetworksComputer-Aided Civil and Infrastructure Engineering, 2017
- Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification2016
- Kinect‐Based Pedestrian Detection for Crowded ScenesComputer-Aided Civil and Infrastructure Engineering, 2016
- A multi-camera video dataset for research on high-definition surveillanceInternational Journal of Machine Intelligence and Sensory Signal Processing, 2014
- Laying out the occupant flows in public buildings for operating efficiencyBuilding and Environment, 2012
- A review on vision-based pedestrian detection in intelligent transportation systems2012
- A comparison of three methods for assessing the walkability of the pedestrian environmentJournal of Transport Geography, 2011
- Association between Walking Speed and Age in Healthy, Free-Living Individuals Using Mobile Accelerometry—A Cross-Sectional StudyPLOS ONE, 2011
- Analysis of factors affecting the choice of route of pedestriansTransportation Planning and Technology, 1985