71 papers with code • 6 benchmarks • 14 datasets
Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task.
In this paper, we propose a simple yet effective crowd counting and localization network named SCALNet.
To better enhance the adversarial robustness of crowd counting models, we propose the first regression model-based Randomized Ablation (RA), which is more sufficient than Adversarial Training (ADT) (Mean Absolute Error of RA is 5 lower than ADT on clean samples and 30 lower than ADT on adversarial examples).
Current weakly-supervised counting methods adopt the CNN to regress a total count of the crowd by an image-to-count paradigm.
In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single-column encoder-decoder architecture for crowd counting, which consists of an encoder based on a dense context-aware module (DCAM) and a hierarchical attention-guided decoder.
In this paper, we propose a novel map for dense crowd localization and crowd counting.
In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer.
Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc.