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Found 18 papers, 8 papers with code
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks.
Pixel-wise Crowd Understanding via Synthetic Data
To be specific, 1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; 2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels.
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images.
Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
Specifically, for a specific neuron of a source model, NLT exploits few labeled target data to learn domain shift parameters.
CNN-based Density Estimation and Crowd Counting: A Survey
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
Pixel-Level Self-Paced Learning for Super-Resolution
Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields.
Focus on Semantic Consistency for Cross-domain Crowd Understanding
According to the semantic consistency, a similar distribution in deep layer's features of the synthetic and real-world crowd area, we first introduce a semantic extractor to effectively distinguish crowd and background in high-level semantic information.
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc.
Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
To reduce the gap, in this paper, we propose a domain-adaptation-style crowd counting method, which can effectively adapt the model from synthetic data to the specific real-world scenes.
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction
Recently, crowd counting using supervised learning achieves a remarkable improvement.
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
The latter attempts to extract more discriminative features among different channels, which aids model to pay attention to the head region, the core of crowd scenes.
C^3 Framework: An Open-source PyTorch Code for Crowd Counting
This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F).
Graph Convolutional Tracking
To comprehensively leverage the spatial-temporal structure of historical target exemplars and get benefit from the context information, in this work, we present a novel Graph Convolutional Tracking (GCT) method for high-performance visual tracking.
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion.
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling
Our contributions are threefold: (1) A priori s-CNNs model that learns priori location information at superpixel level is proposed to describe various objects discriminatingly; (2) A hierarchical data augmentation method is presented to alleviate dataset bias in the priori s-CNNs training stage, which improves foreground objects labeling significantly; (3) A soft restricted MRF energy function is defined to improve the priori s-CNNs model's labeling performance and reduce the over smoothness at the same time.
Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection
Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving.
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes
In this paper, we propose a weakly supervised adversarial domain adaptation to improve the segmentation performance from synthetic data to real scenes, which consists of three deep neural networks.
Learning from Synthetic Data for Crowd Counting in the Wild
Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data; 2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations.
