Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method
We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++)
that contains “4,372” images with “1.51 million” annotations. In comparison to existing …
that contains “4,372” images with “1.51 million” annotations. In comparison to existing …
Cnn-based density estimation and crowd counting: A survey
Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …
meaningful task and has been applied in many applications such as urban planning and …
Cross-view cross-scene multi-view crowd counting
Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend
the field-of-view of a single camera, capturing more people in the scene, and improve …
the field-of-view of a single camera, capturing more people in the scene, and improve …
Autoscale: Learning to scale for crowd counting
Recent works on crowd counting mainly leverage Convolutional Neural Networks (CNNs) to
count by regressing density maps, and have achieved great progress. In the density map …
count by regressing density maps, and have achieved great progress. In the density map …
Crowd counting with partial annotations in an image
To fully leverage the data captured from different scenes with different view angles while
reducing the annotation cost, this paper studies a novel crowd counting setting, ie only using …
reducing the annotation cost, this paper studies a novel crowd counting setting, ie only using …
Towards using count-level weak supervision for crowd counting
Most existing crowd counting methods require object location-level annotation which is labor-
intensive and time-consuming to obtain. In contrast, weaker annotations that only label the …
intensive and time-consuming to obtain. In contrast, weaker annotations that only label the …
Locating and counting heads in crowds with a depth prior
To simultaneously estimate the number of heads and locate heads with bounding boxes, we
resort to detection-based crowd counting by leveraging RGB-D data and design a dual-path …
resort to detection-based crowd counting by leveraging RGB-D data and design a dual-path …
Stacked homography transformations for multi-view pedestrian detection
Multi-view pedestrian detection aims to predict a bird's eye view (BEV) occupancy map from
multiple camera views. This task is confronted with two challenges: how to establish the 3D …
multiple camera views. This task is confronted with two challenges: how to establish the 3D …
Deep learning based crowd counting model for drone assisted systems
M Woźniak, J Siłka, M Wieczorek - … of the 4th ACM MobiCom workshop …, 2021 - dl.acm.org
Recent advances in deep learning make it possible to implement neural network
architecture fitted to the task. In this paper we present new deep neural network model …
architecture fitted to the task. In this paper we present new deep neural network model …
Calibration-free multi-view crowd counting
Deep learning based multi-view crowd counting (MVCC) has been proposed to handle
scenes with large size, in irregular shape or with severe occlusions. The current MVCC …
scenes with large size, in irregular shape or with severe occlusions. The current MVCC …