14 results about "Crowd counting" patented technology

The application discloses a crowd counting method and device, a terminal equipment and a storage medium. The method comprises the following steps: acquiring a target picture; performing prediction on the target picture by using a pre-trained crowd counting model to obtain a density map prediction value and a head number prediction value; and performing weighted calculation on the density map prediction value and the head number prediction value to obtain a crowd quantity count value. The head number prediction value is used as an auxiliary result to be weighted with the density map prediction value, so that the accuracy of the obtained crowd quantity count value is improved, and the accuracy of crowd counting is improved.

The present application relates to the field of transportation information engineering, and more particularly to a traffic hub crowd counting method and system based on multi-scale cross-region graph convolution driving, wherein the method comprises: constructing a crowd counting sequence; constructing three time series density map identification evaluation standards of spatial consistency, time sequence continuity and scale adaptability; proposing a multi-scale cross-region graph convolution network model to train and test the traffic hub crowd counting; and performing instance verification of the traffic hub crowd counting. The proposed model is applied to a series of empirical data sets for training and testing, which proves that it can achieve better demand prediction performance than the baseline model.

The application discloses a crowd counting method in a complex scene based on a visual angle self-adaption, and the counting method comprises the following steps: a NOOMP framework is fitted to a natural world through a few-shot learning method; the NOOMP framework is trained through meta-learning to adapt a multi-head parallel network to a main body of the NOOMP framework; the multi-head parallel network is used for estimating a density map of the crowd; and the multi-head parallel network is trained in multiple different scenes, and sub-losses are summarized. The application proposes a new marking method, an absolute geometric Gaussian generation method, and the method can obtain better precision by only adding a point to each person in an image.

This invention provides a multimodal crowd counting method based on multi-scale alignment and fusion. First, a multimodal crowd scene dataset is acquired and divided into training, validation, and test sets. Preprocessed RGB images and multimodal auxiliary images are input into a VGG16 backbone network to extract multi-stage high-level feature maps. These are then sequentially fed into a density-sharing local contrastive learning module, a local feature fusion module, and an adaptive Mamba context-aware fusion module to achieve cross-modal fine-grained alignment, local feature fusion, and global context modeling. Subsequently, the global fused features are input into a dynamically upsampled multi-scale feature decoder to generate a high-resolution crowd density map. Supervised training is performed using a composite loss function, and the optimal model is saved for testing. This invention adopts an "align-then-fuse" architecture, effectively mitigating cross-modal heterogeneity and density fluctuation problems through multi-module collaborative design, significantly improving the accuracy and robustness of crowd counting in complex scenes.

This invention discloses a video crowd counting method based on temporal interaction and a global association network, belonging to the field of crowd counting technology. It involves acquiring a continuous video sequence of a preset length containing pedestrians, forming a sample set with T consecutive video frames and the ground truth density map corresponding to each of the T consecutive video frames. Based on the sample set, a training model including an encoding module, a dual-branch feature fusion module, a channel-guided cross-branch feature fusion module, and a feature integration module is trained to obtain a temporal interaction and global association network used to generate the predicted density map of T consecutive frames. The T consecutive video frames to be estimated are input into the trained temporal interaction and global association network to obtain the predicted density map of T consecutive frames. The crowd estimation result is generated by summing the results frame by frame. This invention effectively improves the accuracy and robustness of crowd counting in video scenes through dual-branch parallel processing and cross-dimensional feature fusion.

The application discloses a kind of multi-modal crowd counting model construction methods, comprising: extracting multi-modal feature from multi-modal source signal;Set learnable counting feature;Cascade multi-modal feature and counting feature, form the fusion feature of counting guidance;Through multi-head self-attention block, the fusion feature of counting guidance is enhanced, and enhanced feature is formed;Split enhanced feature, form enhanced multi-modal feature and enhanced counting feature;Enhanced multi-modal feature channel cascade is used Prediction head carries out the prediction of density map;Using multilayer perception, enhanced counting feature is reduced channel, and forms count value;Using density map true value supervises density map, using count value supervises the count value of density map statistics, using count value supervises;Through training set training forms multi-modal crowd counting model.The model constructed by the application can improve crowd counting precision by the guidance of counting information, multi-modal fusion is implemented by multi-head self-attention.

The application discloses a cross-modal crowd counting system and method based on a Transformer, and steps are as follows: inputting an infrared thermal image and a visible light image to a first-order feature extraction module to obtain first-order features; obtaining first-order cross-modal features and attention weights through a first-order modal mixer; obtaining first-order enhanced features through element-by-element multiplication and addition operations; inputting the enhanced visible light and infrared thermal features into a second-order feature extraction module to obtain second-order features; inputting the second-order features into a second-order modal mixer to obtain second-order cross-modal features and attention weights; obtaining second-order enhanced features through element-by-element multiplication and addition operations; inputting the enhanced visible light and infrared thermal features into a third-order feature extraction module to obtain third-order features; inputting the enhanced visible light and infrared thermal features into a third-order modal mixer to obtain third-order cross-modal features; and inputting the cross-modal features of the three stages into a regression head to obtain an estimated density map.

This invention discloses a video crowd counting method, apparatus, and computer-readable storage medium, relating to the field of video crowd counting technology. The method includes acquiring a sequence of video frames to be processed; for each frame in the video frame sequence, generating a motion guide map based on the current frame and its temporally adjacent frames; extracting features from multiple consecutive frames in the video frame sequence and their corresponding motion guide maps to obtain static appearance features and dynamic motion features for each frame; fusing the static appearance features and dynamic motion features through a pre-constructed multi-path fusion network to obtain the final features; outputting a crowd density map of the current frame based on the final features, and calculating the estimated number of people in the current frame based on the crowd density map; the multi-path fusion network includes a dual-path reconstruction module and a global temporal context aggregation module.

The application discloses a kind of crowd counting method, device, equipment and computer readable storage medium.The method comprises: determining the region model corresponding to target area provided with multiple photographic devices;Multiple photographic data respectively photographed by multiple photographic devices are analyzed based on region model, and multiple crowd analysis data are obtained;According to multiple simulation photography range, multiple crowd analysis data are analyzed, and crowd counting result is obtained.Because in region model, target area is covered by multiple simulation photography range, and adjacent and no overlapping simulation between these simulation photography range, by region model, it can be seen that, actual need not to adjust the deployment position of photographic device to make target area completely located in the shooting range, facilitate crowd counting, improve crowd counting efficiency.In the case where simulation target area is covered by these simulation photography range without overlap, the situation of missing or repeated counting can be avoided, and the crowd counting effect is improved.

This invention discloses a crowd counting method and system based on a deep convolutional neural network, relating to the field of computer vision technology. The method includes: acquiring original images of dense crowds; uniformly dividing the original images into multiple image patches of uniform size; numbering each image patch and recording the number of people in each patch to generate a training dataset; constructing a deep convolutional neural network based on detail paths and context paths; training the deep convolutional neural network using the training dataset to obtain a crowd prediction model; estimating the number of people in each image patch using the crowd prediction model; and summing the number of people in all image patches to obtain the total number of people in the original image; wherein the detail paths and context paths perform parallel feature extraction on the acquired dense crowd images. This invention achieves synergistic optimization of the efficiency and performance of feature extraction from dense crowd images, providing important technical support for crowd counting tasks in complex scenes.

A method includes performing background data collection in a first region at a first time, the performing background data collection in the first region includes determining a first set of time-averaged reference signal received power (RSRP) data for each beam identification (ID) number and a first set of standard deviation data of the time-averaged RSRP for each beam ID number based on a first set of RSRP data. The method further includes performing RSRP data collection in the first region for a second time and performing RSRP data processing for a first database, and performing k-nearest neighbor (kNN) crowd counting based on at least the RSRP data processing. The performing kNN crowd counting includes estimating a number of people in the first region based on at least a second set of normalized statistical features.

The application discloses a kind of crowd counting methods based on multi-directional audio auxiliary, the counting method includes the following steps: VCC module combines the hollow convolution of multiple size convolution kernel, extracts crowd feature from image;By adaptively encoding multi-level context information into the features generated by VCC module, visual features are extracted;ACC module carries out preliminary fusion and feature extraction to original multi-segment multidirectional audio;The multi-channel visual features of VCC module and the multidirectional audio features of ACC module are fused, and then a single convolution operation is performed to generate a density map.The framework proposed by the application solves the perspective problem, low-light scene and the influence of different positions on audio feature extraction, and it is the first time to use multi-segment multidirectional audio to assist crowd counting in the field of crowd counting.

This invention discloses a self-supervised crowd counting method based on color and texture features, comprising three stages: In the self-supervised pre-training stage, firstly, pseudo-labels for crowd counting are generated by aggregating pixels with similar color or texture features from a large number of unlabeled crowd images. Then, a crowd counting model based on an improved U-Net is constructed, and self-supervised pre-training is performed using the unlabeled crowd images and their pseudo-labels to obtain the pre-trained model. In the supervised fine-tuning stage, the pre-trained model is fine-tuned using a small number of labeled crowd images to obtain the target model. In the prediction stage, the crowd image to be counted is input into the target model, and the crowd counting result for that image is output. This invention uses pseudo-labels for crowd counting in self-supervised pre-training, making the pre-training process closely related to the subsequent crowd counting task. When fusing shallow and deep features, spatial and channel attention are used to enhance features, improving the model's counting accuracy.

This invention relates to the field of crowd instance segmentation technology, and provides a crowd instance segmentation method and system based on mask optimization and enhanced point selection. The method includes: performing a dense region point-to-mask optimization step on the current crowd image based on the location coordinates of the individuals to be segmented in the current crowd image and the image segmentation basic model, to obtain a predicted mask segmentation result; processing the predicted mask segmentation result using a crowd counting model to obtain preliminary predicted points; sampling the preliminary predicted points and combining the sampled points with their corresponding preliminary predicted points to form a new set of predicted points; performing the dense region point-to-mask optimization step again on each set of new predicted points for segmentation; assigning the highest score to the predicted point in each set whose predicted mask is closest to the real mask; selecting the highest-scoring predicted mask segmentation result from each set; and then training the image segmentation basic model to obtain a trained image segmentation model, which can improve the accuracy of crowd instance segmentation.