most citedLDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds

7 citations · 28 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20222 cited

Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects

Qi Wang, Juncheng Wang, Junyu Gao +2

The mainstream crowd counting methods regress density map and integrate it to obtain counting results. Since the density representation to one head accords to its adjacent distribu…

cs.CV20221 cited

DR.VIC: Decomposition and Reasoning for Video Individual Counting

Tao Han, Lei Bai, Junyu Gao +2

Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, crossline crowd coun…

cs.CV20216 cited

Audio-visual Representation Learning for Anomaly Events Detection in Crowds

Junyu Gao, Maoguo Gong, Xuelong Li

In recent years, anomaly events detection in crowd scenes attracts many researchers' attention, because of its importance to public safety. Existing methods usually exploit visual…

cs.CV20212 cited

Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification

Qi Wang, Sikai Bai, Junyu Gao +2

Person re-identification (re-ID) has gained more and more attention due to its widespread applications in intelligent video surveillance. Unfortunately, the mainstream deep learnin…

cs.CV20217 cited

LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds

Qi wang, Tao Han, Junyu Gao +2

The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map. It also enlightens us back…

cs.CV20214 cited

Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer

Junyu Gao, Maoguo Gong, Xuelong Li

Crowd localization is a new computer vision task, evolved from crowd counting. Different from the latter, it provides more precise location information for each instance, not just…