Related references
Note: Only part of the references are listed.Crowd density estimation based on classification activation map and patch density level
Liping Zhu et al.
NEURAL COMPUTING & APPLICATIONS (2020)
People Counting in Dense Crowd Images Using Sparse Head Detections
Mamoona Birkhez Shami et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2019)
Multi-resolution attention convolutional neural network for crowd counting
Youmei Zhang et al.
NEUROCOMPUTING (2019)
Congestion detection in pedestrian crowds using oscillation in motion trajectories
Sultan Daud Khan
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)
Crowd Counting Using Scale-Aware Attention Networks
Mohammad Asiful Hossain et al.
2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV) (2019)
DISAM: DENSITY INDEPENDENT AND SCALE AWARE MODEL FOR CROWD COUNTING AND LOCALIZATION
Sultan Daud Khan et al.
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) (2019)
Scale Driven Convolutional Neural Network Model for People Counting and Localization in Crowd Scenes
Saleh Basalamah et al.
IEEE ACCESS (2019)
Pushing the Limits of Deep CNNs for Pedestrian Detection
Qichang Hu et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2018)
Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes
Afshin Dehghan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)
Anomalous entities detection and localization in pedestrian flows
Habib Ullah et al.
NEUROCOMPUTING (2018)
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
Zan Shen et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)
What Can Help Pedestrian Detection?
Jiayuan Mao et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
Shiyu Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Densely Connected Convolutional Networks
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning
Haroon Idrees et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2015)
Fast Feature Pyramids for Object Detection
Piotr Dollar et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2014)
Tracking in dense crowds using prominence and neighborhood motion concurrence
Haroon Idrees et al.
IMAGE AND VISION COMPUTING (2014)
Switchable Deep Network for Pedestrian Detection
Ping Luo et al.
2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2014)
Selective Search for Object Recognition
J. R. R. Uijlings et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2013)
Crowd disasters as systemic failures: analysis of the Love Parade disaster
Dirk Helbing et al.
EPJ DATA SCIENCE (2012)
Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional Features
Junping Zhang et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2011)
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching
Zhe Lin et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2010)
A Method for Counting Moving People in Video Surveillance Videos
Donatello Conte et al.
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING (2010)
Efficient belief propagation for early vision
Pedro F. Felzenszwalb et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2006)
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton et al.
NEURAL COMPUTATION (2006)
Complete solution classification for the Perspective-Three-Point problem
XS Gao et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2003)
Share Paper
