100 results about "Crowd counting" patented technology

The invention provides a multi-modal crowd counting method and system, and the method comprises the steps: obtaining an image, and extracting the image features of the image, the image comprising a visible light modal image and a thermal infrared modal image; image features of the visible light modal image and the thermal infrared modal image are subjected to attention through spatial frequency in a spatial frequency guiding module layer by layer to generate a target attention map; fusing the image features of the visible light modal image and the thermal infrared modal image of each level and the target attention map through an adaptive dynamic fusion module to obtain each multi-modal fusion feature; according to the multi-modal crowd counting method, a prediction density map is generated from each multi-modal fusion feature through a multi-scale progressive fusion module, the multi-scale progressive fusion module is composed of cavity space pyramid pooling and Swin Transform Block, and through the modules designed in the steps, the problems that multi-modal crowd counting errors are large and counting precision is low can be effectively solved.

The invention relates to the technical field of crowd counting, in particular to a crowd counting method based on a DGCC-Net model, and the method comprises the steps: obtaining a crowd counting image; a DGCC-Net model is constructed, and pre-training is carried out; extracting a multi-scale feature map from the crowd counting image by using a feature extraction network; a local attention module is used for extracting context information and detail information of a crowd region from a feature map on a high-resolution scale in the multi-scale feature map, and a high-resolution feature map is obtained; feature fusion of semantic and detail information is carried out on a feature map on a low-resolution scale and a high-resolution feature map in the multi-scale feature map by using a multi-level feature fusion network, and a density guide feature map is generated from the fused feature map through a density guide module; and predicting a crowd counting result according to the density guide feature map by using a prediction network. According to the invention, the counting precision and robustness are improved when crowd scenes with uneven density distribution are processed.

The invention discloses a video crowd counting method based on a cascaded cross-domain feature interaction network. The method comprises the following steps: carrying out data enhancement processing of random cutting and horizontal flipping on a current frame and front and back frames of the current frame; and constructing a cross-domain feature interaction network composed of a spatial domain branch and a frequency domain branch. The frequency domain branch extracts frequency domain feature output of different stages through a high and low frequency signal aggregation module and a feature encoder based on adjacent frames; the spatial domain branch is based on a single-frame image, and static spatial semantic features are extracted through a feature encoder. Cascade fusion is carried out on the double-branch features on multiple scales, two-way channel cross attention is utilized to reconstruct time sequence correlation frequency domain features of a current frame, and fusion and reconstruction of the two domain features are achieved through a cross-domain feature mutual modulation module. And after the reconstructed double-branch features are processed by the fusion network, outputting a crowd density map of the current frame by a density regression head. And after training is completed, storing the optimal model for video crowd counting. According to the invention, through cross-domain feature cascade and bidirectional time sequence modeling, the accuracy and robustness of crowd counting in a video scene are effectively improved.

The invention discloses a multi-scale crowd counting method and system based on VSSM and mask reconstruction and a medium, and belongs to the field of computer vision and deep learning, and the method comprises the steps: carrying out the multi-scale transformation and segmentation operation of an input crowd image, so as to obtain an image block under each scale; analyzing each image block to obtain an information entropy graph, performing masking operation on each image block according to the information entropy graph, inputting the masked image block into a VSSM to perform feature extraction so as to obtain coding features, performing decoding reconstruction on the coding features through a Transform decoder, and finally obtaining a reconstructed feature image; fusing the reconstructed feature images of different scales by using a multi-scale fusion module to generate fused features; and generating a crowd density map based on the fusion features, and analyzing the crowd density map to obtain a crowd counting result. Counting errors caused by single-scale analysis are avoided, and the crowd counting precision in a full-scale range is remarkably improved.

The invention discloses a lightweight crowd counting and positioning method, and the method comprises the steps: building a brand-new lightweight crowd counting and positioning network, designing a grouping feature pairing interaction module, carrying out the grouping of feature maps, splicing adjacent groups, learning the context relation between feature groups, generating a dynamic attention weight to enhance the expression of key features, and carrying out the recognition of the key features. Therefore, the recognition capability of the target in the complex shielding scene is improved. A multi-stage training strategy is adopted, backbones, positioning branches, segmentation branches and lightweight adapter modules are sequentially and independently trained, and stable convergence of the multi-task capability of the model is ensured; according to the method, excellent performance is achieved on the public crowd counting data set, counting and positioning precision is improved, low calculation overhead is kept, and an efficient and accurate solution is provided for crowd analysis tasks in a resource limited scene.

The invention discloses a dynamic label flipping backdoor attack method facing RGB-T crowd counting, and belongs to the technical field of neural networks. The method comprises the following steps: randomly selecting 10% of all samples as poisoning samples for pollution, and taking the rest 90% as benign samples; designing a local disturbance trigger and embedding the local disturbance trigger into RGB image information of the poisoning sample; designing a local patch trigger and embedding the local patch trigger into T image information of the poisoning sample; designing a label information modification strategy and finishing modification of poisoning sample label information; the poisoning and benign samples are mixed according to a preset proportion to serve as a training set to train any RGB-T crowd counting model, and an RGB-T crowd counting poisoning model with a backdoor function is generated; the intoxication model is used to test clean and intoxication test sets respectively to assess the attack performance thereof. According to the method, different triggers and different label modification strategies are designed according to the difference among the modes, and a new attack and defense reference is provided for security defense research of the multi-mode visual system.

The invention relates to the technical field of crowd counting, in particular to a crowd counting method and system based on WiFi and video modal cross-level attention, and the method comprises the steps: constructing a WiFi density map at a WiFi sensing side, and converting an irregular detection record into a fixed-size image representation; on a video sensing side, marking a region of interest for video frames collected by cameras with different visual angles, and cutting the region of interest to serve as a video side image; respectively carrying out feature coding on the WiFi density map and the video side image by adopting a convolutional neural network and self-attention combined mode; gradually aligning the WiFi modal feature embedded representation and the video modal feature embedded representation through multi-layer stacked cross-modal attention to obtain a cross-modal fusion feature; and inputting the cross-modal fusion features into a lightweight multilayer perceptron, and outputting crowd count. According to the invention, through hierarchical alignment and fusion of WiFi signals and video features, accurate estimation of the number of crowds in a large-scale complex scene can be realized.

The present invention relates to a method for counting the number of people in video images based on a multi-scale attention mechanism, which solves the defect that it is difficult to ensure the accuracy of people counting under the interference of complex scenes compared with the prior art. The present invention includes the following steps: acquisition and preprocessing of crowd images; generation of real crowd density maps; construction of a multi-scale attention mechanism module; construction of a crowd counting model; training of the crowd counting model; acquisition of video images to be detected; counting of the number of people in the video images. The present invention designs a multi-scale attention module, which embeds an attention mechanism in different scale branches to reduce the influence of irrelevant background noise of the model at different scales and increase the scale diversity of the model.

The invention discloses an RGB-T counting method and device based on semantic perception complementary feature mining. The method comprises the steps that firstly, images in a training data set are preprocessed; then constructing an RGB-T counting network based on semantic perception complementary feature mining, wherein the RGB-T counting network comprises a feature extractor based on Transform, a mode feature adaptive method from coarse to fine and a semantic perception agent module; the feature extractor based on the Transform is used for extracting high-level semantic representations of two different modal images; the mode feature adaptive method from coarse to fine is used for filtering noise features and mining fine complementary features; and the semantic perception agent module is used for enhancing semantic consistency perception in network output and the feature extractor, and predicting a density map and a counting result through a density regression layer. According to the method, the RGB-T crowd counting task can be efficiently completed, and the counting result is better than that in the prior art.

The application discloses a kind of multi-modal crowd density prediction methods based on time convolution network, comprising the following steps: video information module obtains image from monitoring camera, the image obtained is carried out crowd counting by crowd counting model, obtains the crowd density of each camera every time, and is organized into time series data;The time series data of crowd density is respectively extracted into the current time hidden vector for each sub-region by time convolution network;Itinerary planning module extracts corresponding itinerary information from itinerary planning table for multi-modal prediction;The features of fusion module and video information module are fused, and the crowd density prediction value of each sub-region in the future is obtained.This application can improve the prediction accuracy of the model by fusing multi-modal information, and can give a smooth prediction result when facing low SNR data, and can also respond in time when the crowd density changes suddenly.

The invention discloses a small target cluster tracking method based on memory enhancement. The method comprises the following steps: 1) constructing a data set of non-repeated crowd counting to a video frame sequence by using aerial photography data; 2) detecting a target by a pre-trained crowd positioning network based on inverse focal length transformation; 3) inputting the video frames and the positioning coordinates into a deepened memory network to extract deepened memory features of the head; 4) constructing a loss function, and training the deepened memory network; 5) tracking the test data set, and constructing a memory item for a tracked trajectory; 6) associating the existing track with the detection target in the current frame; according to the method, a deepened memory network is designed, the network can extract target features with higher discriminability only through human head point annotation, and therefore representation of the foreground head in the head area is remarkably optimized.

The invention discloses a foggy day crowd counting method based on Transform coding cross attention, and the method comprises the steps: obtaining a preprocessed image, carrying out the feature extraction of the preprocessed image through employing a Transform encoder network, and obtaining the feature Fr of the image; inputting the feature Fr of the image into a local enhancement module, and updating and outputting a local enhancement feature F'r; outputting a feature sequence composed of local enhancement features F'r by the local enhancement module, merging the position token into the feature sequence, inputting the feature sequence into the decoder module, and outputting a final decoding feature Wr; and forming a final feature sequence by the final decoding features Wr output by the decoder module, performing global average pooling on the final feature sequence, and feeding the final feature sequence to a regression head to generate a predicted crowd count. Transform is used as a backbone network to better capture crowd structure dependence at different positions, so that the model is more accurate in crowd number estimation in a heavy fog scene, and errors are reduced.

The invention belongs to the technical field of crowd counting, and particularly relates to a domain-oriented adaptive crowd counting energy-driven active learning method, which comprises the following steps of: extracting multi-scale visual features from an input image, and training HRNet to obtain an optimal source domain model; source domain and target domain samples are mapped to a unified energy space through an energy function; performing data enhancement on the target domain sample, and calculating uncertainty and a predicted people number mean value under various data enhancement modes; calculating sample energy of the target domain; screening the target domain samples twice by adopting an active learning strategy, and labeling the screened samples; designing energy alignment loss; and performing fine tuning on the optimal source domain model, and obtaining a final crowd counting result by using the fine-tuned model. According to the method, data distribution of the source domain and the target domain is effectively aligned through the energy model, sample labeling is carried out in combination with an active learning strategy, and the cross-domain crowd counting performance is improved.

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 invention relates to the technical field of AI patrol robots, in particular to an intelligent scheduling system and scheduling method for AI patrol robots. An intelligent scheduling system of an AI patrol robot comprises a scenic spot modeling device which constructs a scenic spot two-dimensional road network based on a scenic spot route map; the people counting device is used for counting the current tourist number of each node in the two-dimensional road network in real time and generating current people flow data; according to the intelligent scheduling system provided by the invention, the initial patrol plan is generated according to the future people number distribution prediction map of the scenic spot, the dynamic matching precision and adaptability of the patrol strategy and the real-time people flow density are remarkably improved, and the inherent limitation of a fixed route patrol mode is effectively overcome.

The invention discloses a dense crowd counting method based on an improved yolov11 model. The dense crowd counting method comprises the following steps: improving a feature extraction network of a yolov11n model; replacing an original CBS and C3K2 module of the yolov11n model with an encoder module of a U-NET V2 network; the feature fusion network of the yolov11n model is improved; an AIFI module is added behind the highest layer of the feature fusion network; a detection head of the yolov11n model is improved, and a DIOU loss function is adopted to replace an original loss function; and inputting the preprocessed image tensor into the improved yolov11n model for training, and storing the trained model for dense target counting detection. According to the method, the calculation speed is ensured as much as possible, and the precision of crowd number detection when severe shielding exists between targets in a dense scene 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.

The present application relates to the technical field of post safety management, and specifically relates to a personnel off-duty detection method based on a Hungarian algorithm and a P2PNet, which comprises the following steps: step 1, acquiring image information, installing a camera, adjusting the irradiation direction of the camera so that it includes all posts in the monitored area, and collecting image information of on-site personnel under different conditions; step 2, data labeling and model training, labeling the original data set, and training a head center point detection model based on the P2PNet; the present application uses artificial intelligence technology to detect personnel off-duty, combines the crowd counting P2PNet algorithm and the Hungarian matching algorithm, and is deployed on the Cambrian MLU370-S4 intelligent acceleration card, thereby realizing real-time automatic detection of personnel off-duty. The method has high robustness and reliability for various scenes, and the intelligent acceleration card ensures the timeliness of the detection, reduces a large amount of labor cost, and ensures the safety of production operations.

This invention discloses a crowd counting and localization method based on adaptive analysis of dense regions, belonging to the field of target detection. The method first constructs a dense crowd localization dataset for training. Secondly, a deep target detection network model is designed to learn the mapping relationship between input images and crowd locations, implementing an end-to-end prediction model. The method then roughly estimates the crowd density of the image under test, and then performs a secondary detection of the dense crowd image area to achieve rapid and accurate counting and localization of dense crowds. This method is simple and easy to implement, and can achieve efficient and accurate crowd counting and localization.

The invention discloses a cross-line crowd counting method based on individual space-time coupling feature consistency, which comprises the following steps: 1) inputting a plurality of continuous frames into a crowd density map prediction network based on space-time coupling features, and predicting a density map by using the crowd density map prediction network; 2) extracting positioning points representing individuals by using a local maximum detection algorithm, and extracting detection features of the individuals through the positioning points; 3) constructing an individual recognition network to perform recognition training on detection features of individuals; (4) associating individuals in different frames by using an individual association strategy, skipping the frame of the missing detection individual, and eliminating the error detection individual, and (5) carrying out the cross-line crowd counting test on the test data set in different scenarios. The method has important significance for counting the crowd scale and analyzing the crowd behavior in different geographic information environments.

A weakly supervised crowd counting method based on a multi-scale dynamic graph convolution network belongs to crowd counting in the fields of public security, city planning and traffic scheduling. Due to the complexity and diversity of traffic scenes, it is very difficult to perform point-level labeling on a large number of crowds, and a large amount of manpower is required. Weakly supervised crowd counting is more suitable for these scenes because they only require counting-level annotations. Existing weakly supervised crowd counting ignores the non-uniformity of cross-distance crowd density distribution and multi-scale crowd head, and cannot obtain similar accurate counting results as the fully supervised crowd counting method. The present application proposes a multi-level regional dynamic graph convolution module to extract the internal relationship between different crowd regions, so as to learn dynamic regional scores and further optimize regional feature representation, and a coarse-grained multi-level feature fusion module is designed to extract multi-scale crowd head information. The present application has high regression accuracy and end-to-end crowd counting capability.

The present invention discloses a construction scene crowd counting method based on cross-layer connection of dilated convolution, which comprises the following steps: (1) obtaining a public crowd counting dataset and generating a true density map based on manual annotation; (2) establishing a construction scene crowd counting network CL-DCNN based on cross-layer connection of dilated convolution; (3) inputting a crowd image in the dataset into the crowd counting network CL-DCNN, which outputs a predicted density map of the image; (4) integrating and summing the output predicted density map to obtain the total number of people in the image, performing loss calculation between the output predicted density map and the true density map, and continuously iterating and updating the parameters in the crowd counting network; (5) inputting a test image in a construction scene into the trained crowd counting network, generating a crowd density map, and integrating and summing the map to obtain a crowd counting result. The present invention has good adaptability and high prediction accuracy.

The present disclosure relates to a crowd counting method and device, an electronic device and a storage medium. The method comprises: obtaining a crowd image; obtaining a first number of people corresponding to the crowd image and a first crowd density distribution map corresponding to the crowd image based on head key point positioning on the crowd image; obtaining a second crowd density distribution map corresponding to the crowd image based on crowd density detection on the crowd image; selecting a target crowd density distribution map corresponding to the crowd image from the first crowd density distribution map and the second crowd density distribution map based on the first number of people and a first preset number threshold; and determining a crowd counting result of the crowd image based on the target crowd density distribution map. The embodiments of the present disclosure can improve the accuracy of crowd counting in various scenarios.

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 invention discloses an unmanned aerial vehicle aerial image dense crowd counting and grade classification method based on a multilayer CNN, and the method comprises the following steps: carrying out the standardization preprocessing of an input unmanned aerial vehicle aerial image I; multi-scale features of the image are extracted through multi-layer convolution and pooling operation, and a high-level semantic feature map F is obtained; a context aggregation module is utilized, adaptive average pooling of different scales is adopted to capture global to local context information, and a feature map F'with attention weight is generated through convolution fusion; a rear-end decoder recovers spatial details by using cavity convolution, a single-channel density map D is output through convolution operation, and a crowd counting result is obtained through integration; and finally, based on the density map D, dividing four density intervals through a threshold value: analyzing and marking a region above medium density by using 8 connected domains, and outputting a visualization result on the image I. According to the invention, grading and counting of crowd density in an unmanned aerial vehicle aerial photographing scene can be accurately realized, and applications such as public safety monitoring and large-scale activity management can be effectively supported.

The present application provides a new crowd counting method. First, the first 16 layers of VGG19 are used as the backbone network to extract shallow features, and then a double-branch structure is used in the feature extraction module. Branch 1 uses a pyramid structure with a fusion self-attention mechanism, and the feature map generated by the pyramid structure is sent to a transition residual block to generate the feature map of branch 1. Branch 2 uses a double-channel attention module, and the feature maps obtained by branches 1 and 2 are sent to a transition residual block for splicing and fusion. The fused feature map is sent to a transition module to generate the final feature map. Finally, the final feature map is sent to a 1x1 convolution to generate a density map. During model training, the present application uses a joint loss function to minimize the influence of outliers on the entire model. Branch 1 of the present application can accurately locate targets of different scales and depict the spatial dependency between any two positions in the feature map. Branch 2 of the present application can focus on important features in the crowd, thus achieving excellent crowd counting performance.

The application provides a construction method of a multi-dimensional dynamic perception and progressive focusing network model for crowd counting, and relates to the technical field of computer vision, and comprises: a front-end sub-network used for shallow image feature extraction on a preprocessed input image to obtain image shallow features; a main body sub-network used for multi-branch feature interaction based on the image shallow features to generate deep features and cross-layer features, cross attention processing and multi-scale feature fusion on the deep features and the cross-layer features, cross-dimensional attention modeling on the fused features, and layer normalization output of depth features; and a rear-end sub-network used for high-dimensional feature construction and spatial resolution adjustment on the depth features to obtain intermediate features, feature optimization on the intermediate features, attention fusion and density decoding, and output of a density map and a target attention map. The network model can realize crowd counting in a wide-area scene.

The application provides a dense crowd counting method combining a high-resolution CNN and a lightweight Transformer, and comprises the following steps: the scale size of a human head in a crowd image is calculated by using a fixed Gaussian kernel method to generate a supervised density map for network training; a crowd counting network based on a high-resolution feature extraction network HRNet and a lightweight Transformer is constructed; data augmentation is performed on a crowd data set, the constructed counting network is trained by using a training set, and an optimal model is screened and saved; the optimal network model obtained is tested by using a test set, and the final counting result of the picture crowd is obtained by accumulating and summing the pixel values of the network predicted density map. The application can not only maintain high-resolution output of crowd features, but also can fuse multi-scale information, improve the robustness of crowd counting, and significantly improve the convergence speed and generalization performance of the model.