Towards Using Count-level Weak Supervision for Crowd Counting

  title={Towards Using Count-level Weak Supervision for Crowd Counting},
  author={Yinjie Lei and Yan Liu and Pingping Zhang and Lingqiao Liu},
  journal={Pattern Recognit.},
TransCrowd: Weakly-Supervised Crowd Counting with Transformer
The proposed TransCrowd is proposed, which reformulates the weakly-supervised crowd counting problem from the perspective of sequence-to-count based on Transformer, and is the first work to adopt a pure Transformer for crowd counting research.
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, i.e. only using partial
Boosting Crowd Counting with Transformers
A pure transformer is used to extract features with global information from overlapping image patches to predict the total person count of the image through regression-token module (RTM) and proposes a tokenattention module (TAM) to recalibrate encoded features through channel-wise attention informed by the context token.
CCTrans: Simplifying and Improving Crowd Counting with Transformer
This paper proposes a simple approach called CCTrans to simplify the design pipeline for crowd counting by utilizing a pyramid vision transformer backbone to capture the global crowd information, a pyramid feature aggregation (PFA) model to combine low-level and high-level features, and an efficient regression head with multi-scale dilated convolution (MDC) to predict density maps.
Crowd Counting Using End-to-End Semantic Image Segmentation
This paper proposed an end-to-end semantic segmentation framework for crowd counting in a dense crowded image based on semantic scene segmentation using an optimized convolutional neural network and successfully highlighted the foreground and suppressed the background part.
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
In image/video analysis, crowds are actively researched, and their numbers are counted. In the last two decades, many crowd counting algorithms have been developed for a wide range of applications in
Deep learning based crowd counting model for drone assisted systems
New deep neural network model developed for drone assisted systems, in which image from drone camera is processed for smart crowd counting operation, works to estimate the crowd in the image by using derivative of ResNet conception model.
Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning
The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN and the result of the research is a published database with the detected number of pedestrians.
Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
A novel Hierarchical Paired Channel Fusion Network (HPCFNet) is presented, which utilizes the adaptive fusion of paired feature channels to adapt to the scale and location diversities of the scene change regions.
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks
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Bayesian Loss for Crowd Count Estimation With Point Supervision
This work proposes Bayesian loss, a novel loss function which constructs a density contribution probability model from the point annotations, and outperforms previous best approaches by a large margin on the latest and largest UCF-QNRF dataset.
Context-Aware Crowd Counting
This paper introduces an end-to-end trainable deep architecture that combines features obtained using multiple receptive field sizes and learns the importance of each such feature at each image location, which yields an algorithm that outperforms state-of-the-art crowd counting methods, especially when perspective effects are strong.
Almost Unsupervised Learning for Dense Crowd Counting
Grid Winner-Take-All (GWTA) autoencoder is developed to learn several layers of useful filters from unlabeled crowd images to achieve superior results compared to other unsupervised methods and stays reasonably close to the accuracy of supervised baseline.
Towards Perspective-Free Object Counting with Deep Learning
A novel convolutional neural network solution, named Counting CNN (CCNN), formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps, able to estimate object densities in different very crowded scenarios.
Gaussian Process Density Counting from Weak Supervision
A weakly supervised kernel learner is devised that achieves higher count accuracies than previous counting models and imposes non-negativeness and smooth the GP response as an intermediary step in model inference.
Crowd Counting Using Multiple Local Features
An approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes is proposed.
Mask-Aware Networks for Crowd Counting
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PaDNet: Pan-Density Crowd Counting
The proposed Pan-Density Network (PaDNet) achieves state-of-the-art recognition performance and high robustness in pan-density crowd counting.
Feature-aware Adaptation and Structured Density Alignment for Crowd Counting in Video Surveillance
  • Junyu Gao, Qi Wang, Yuan Yuan
  • Computer Science
  • 2019
This paper proposes a domain-adaptation-style crowd counting method, which can effectively adapt the model from synthetic data to the specific real-world scenes, and outperforms the state-of-the-art methods for the same cross-domain counting problem.
Fully Convolutional Crowd Counting on Highly Congested Scenes
The state-of-the-art for crowd counting in high density scenes is advanced by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016), and a training set augmentation scheme that minimises redundancy among training samples to improve model generalisation and overall counting performance is developed.