36 results about "Crowd monitoring" patented technology

The invention provides a selective feature background subtraction method aiming at a thick crowd monitoring scene. Aiming at a problem that a traditional background subtraction method can cause higher missed inspection and error inspection rate under a thick crowd scene, the invention discloses the selective feature background subtraction method, which comprises the following steps of: creating a scene sparsity model; selecting a video frame having higher similarity to the sparsity model as a training sample, and obtaining an initialized feature scene based on batching principal component analysis; updating the scene sparsity model, selecting a video frame having higher similarity to the sparsity model to update the feature background by incremental principal component analysis; selectively rebuilding the background at pixel level; and solving an adaptive threshold to threshold a difference image and obtain a foreground image. The selective feature background subtraction method can inspect out slowly moving and static foreground objects well in the thick crowd scene with relatively steady light and simultaneously keep lower error inspection rate.

The invention provides a method for judging crowd density and number of people based on a fish eye camera. The method comprises the following steps of: (1) acquiring a background image; (2) acquiring a crowd monitoring video image; (3) preprocessing the crowd monitoring video image; (4) performing foreground segmentation; (5) extracting a crowd target characteristic; (6) grading the crowd density; and (7) counting the people. The method aims to expand the monitoring range; the problem of inconsistency in sizes of human bodies and the problem of mutual sheltering of the human bodies in the high-density crowd existing in the conventional crowd monitoring method are solved; and the method has good real-time property and can be applied to a real-time crowd monitoring system.

The invention discloses a CAD (computer-aided design) people counting method based on FAST (features from accelerated segment test), and belongs to the field of computer vision-based people counting. The method is characterized in that after a crowd surveillance video image is subjected to filtering preprocessing, an FAST corner feature vector of a current image is obtained through a corner detection algorithm; a low-density crowd image and a high-density crowd image is divided according to the ratio of the number of feature points and the sum of pixels of the current crowd image, and foreground images of the low-density crowd image and the high-density crowd image are extracted; as for the foreground image of the low-density crowd image, the connected domain area T obtained through an erosion algorithm is taken as an FAST point, and as for the foreground image of the high-density crowd image, a neighbor domain is established for the core point of each pixel through an OPTiCS algorithm, then, the minimum reach distance from the core point of each neighbor domain to each pixel is taken as the minimum reach distance in each neighbor domain, and an FAST point vector X of the high-density crowd is constructed accordingly; a crowd evaluation model is constructed according to T, X and the distance D between a camera and the crowd; and a set training sample is taken as a test vector for performing SVM (support vector machine) training, so that the counting speed and the accuracy rate are increased.

The invention discloses an unmanned aerial vehicle ground crowd monitoring system. The system comprises a ground information platform and at least one monitoring unmanned aerial vehicle; the monitoring unmanned aerial vehicle comprises a ground shooting camera, a processor, a wireless transmission mechanism and an illuminance sensor; the processor is bidirectionally connected with the camera and the wireless transmission mechanism; the wireless transmission mechanism wirelessly communicates with the ground information platform; and the illuminance sensor is bidirectionally connected with the camera or the processor. A monitoring method comprises the steps of identifying crowd density through image transparency; coloring crowd images with different densities; and finally calculating out crowd distribution change data. The system has the beneficial effects that the requirements on hardware of the unmanned aerial vehicle are low, and information of crowd number, density, movement and thelike is visually obtained; and the crowd density is colored, so that a manager can visually see a crowd distribution condition, personnel deployment of safety protection is accurately scheduled, the image analysis time is saved, and the working efficiency is improved.

The invention discloses a crowd fleeing event detection method and system based on a direction-clustering model. In order to solve the problem that the crowd fleeing event detection method is low in detection accuracy, the technical scheme comprises the steps of extracting moving tracks from crowd monitoring videos, thereby obtaining a track coordinate matrix, and calculating speeds and directions of moving particles on the moving tracks through the track coordinate matrix; on the basis of the randomness of the speeds and directions of the moving particles, establishing the direction-clustering model which represents a moving state of a crowd; calculating a direction-clustering index of each frame of crowd monitoring video according to the direction-clustering model; extracting an event detection threshold self-adaptively from all obtained direction-cluster indexes, comparing the event detection threshold with the direction-clustering index of each frame of crowd monitoring video, and finally detecting a crowd fleeing event of the crowd monitoring video. The method is widely applied to a middle and high density crowd and the detection accuracy is high.

The invention relates to a cascaded multi-scale-based dense face detection method. According to the method, the detectors with various scale ranges are trained respectively, each object detector is cascaded according to a specific scale range to optimize an existing network structure, and the strategy can be carried in a depth model of face detection, has good expansibility, and is more suitable for dense small face detection. The method can be applied to the specific scenes, such as intensive crowd monitoring, classroom people counting, etc., and has very strong application value.

The invention discloses a community lighting system and method based on crowd activity big data, and relates to the technical field of community lighting systems. The system comprises a community monitoring platform and a plurality of road lamp control terminals. The community monitoring platform is in communication connection with the plurality of road lamp control terminals; the road lamp control terminals are in electric signal connection with two same-side road lamp circuits and a floor lamp circuit; street lamps on the same side of a road are connected in parallel to form a same-side street lamp circuit; and floor lamps on two sides of the road are connected in parallel to form the floor lamp circuit. A camera shoots a plurality of crowd monitoring images and transmits the images to an image analysis module; the image analysis module analyzes the plurality of crowd monitoring images in the same time interval to obtain crowd flow quantity information and transmits the crowd flow quantity information to a crowd activity analysis module; a control module compares a dynamic crowd flow quantity table with a lamp control threshold table to control the two same-side street lamp circuits or the floor lamp circuit to be switched on; and the lighting effect can be guaranteed, and waste can be reduced.

The invention discloses a crowd counting method for a real scene. The method is characterized by adopting a geometric Gaussian adaptive kernel function to generate a real crowd density map as a real value to guide training; inputting the crowd image into the convolution operation of the local receptive field, fusing the generated adaptive feature maps of different scales, inputting the fused feature maps into a deep neural network model, and obtaining the number of people in the scene through regression solution and an integral method according to the obtained estimated density map. Compared with the prior art, the method is simple and convenient, strong in real-time performance and high in accuracy of people counting, can quickly obtain a high-quality crowd density map by adopting a lightweight network structure, does not lose too much statistical precision, occupies a small memory space of model parameters, and is particularly suitable for various scenes with high real-time performance requirements, such as attendance systems, crowd monitoring systems and the like.

The invention discloses a location data real-time wireless transmission method for burst high load condition. The location data real-time wireless transmission method comprises the following steps: (1), setting two threshold values c1 and c2 for number of connections for communication AP, wherein c1 and c2 are natural numbers, c1 is smaller than c2, k is larger than 0 and smaller than 1, k is a real number and is 0.7 generally; C2 is the maximum channel number for wireless protocol support; and (2), selecting the communication AP with the minimum load for connection according to the position of positioning equipment at present and the load of all APs in a range of taking the position as a circle center and semidiameter as r by the positioning equipment, and then uploading location data to the AP. The method is successfully applied to a monitoring system based on wireless indoor positioning; and the practice shows that an indoor positioning system configured with the method can be good in real-time performance in dense crowd monitoring.

The invention provides a crowd monitoring method based on a generative adversarial network, which can be applied to the technical field of artificial intelligence. The method comprises the following steps: training a generative adversarial network, and after training is finished, estimating the number of people in a target monitoring area by using a generator model in the generative adversarial network to obtain a second crowd density estimation graph based on a second crowd image of the target monitoring area, wherein in the training process, a first crowd image is used as input of a generator model, and the generator model is trained to output a first crowd density estimation graph; meanwhile, taking the first crowd density truth value graph and the first crowd density estimation graph as input of a discriminator model so that the discriminator model can discriminate the similarity between the first crowd density truth value graph and the first crowd density estimation graph, and repeating the training process continuously until the discriminated similarity meets a preset threshold condition. The invention further provides a crowd monitoring device and equipment based on the generative adversarial network, a storage medium and a program product.

The invention discloses a 5G planning site recommendation method based on big data, and belongs to the technical field of big data, and the method comprises the steps: building a monitoring region according to the preset length, width and height, carrying out crowd monitoring and environment monitoring of the monitoring region, and obtaining a crowd monitoring set and an environment monitoring set; preprocessing and calculating the crowd monitoring set and the environment monitoring set to obtain a crowd processing set and an environment processing set; obtaining a crowd coefficient and an environment coefficient according to the crowd processing set and the environment processing set, and obtaining a recommendation value of the monitoring area according to the crowd coefficient and the environment coefficient; analyzing the recommendation value to obtain a recommendation result, and recommending the 5G planning site according to the recommendation result; according to the invention, the technical problem of poor planning effect of the 5G planning site in the existing scheme is solved.

The invention discloses a low-complexity dense crowd analysis method based on deep learning. The method comprises the steps of (1) sampling to obtain a picture data set; (2) labeling the picture dataset; (3) selecting a Gaussian convolution kernel, performing Gaussian convolution operation on each marking point of each picture to obtain a corresponding crowd distribution thermodynamic diagram, and counting the real number of pedestrians in each picture; (4) randomly dividing the obtained picture data set into a training set and a test set according to a proportion of 8:2, respectively takingthe training set and the test set as inputs of a training model and a test model, and carrying out offline training to obtain a low-complexity deep learning model for analyzing dense crowd distribution conditions; and (5) decoding the new crowd monitoring video stream in real time, inputting the decoded new crowd monitoring video stream into the training model, representing the crowd distributionsituation by a thermodynamic diagram output by the model, and integrating the thermodynamic diagram to realize real-time analysis of dense crowds. According to the invention, a more excellent crowd monitoring analysis effect can be obtained.

The invention discloses a flood emergency evacuation method based on real-time crowd data fusion. The method comprises the following steps: 1, constructing a two-dimensional hydrodynamic model, and determining the range of a flood risk area and a safe area; 2, developing a position service interface, and accessing the Internet in an emergency state to obtain real-time crowd monitoring data; 3, developing a mobile application of flood emergency evacuation, and collecting user crowd filling data; 4, performing overlay analysis on the LBS real-time crowd monitoring data and the crowd data acquired by the mobile application by adopting a multi-source data fusion technology; 5, constructing a flood emergency evacuation model based on the real-time crowd fusion data, and dynamically planning a crowd evacuation route; and 6, pushing the flood early warning message and the evacuation route information to the crowd in the risk area. The method has the advantages that the accuracy and timeliness of crowd recognition are improved, and the efficiency and effect of emergency risk avoiding and evacuation of crowds in flood risk areas are improved.

The invention discloses a specified crowd monitoring method and device as well as a storage medium, and belongs to the field of computer processing. The method comprises the following steps of acquiring a sound of the surrounding environment of a first terminal to obtain a first voice signal; detecting whether a sound of a specified type exists in the first voice signal or not through a first sound detection module, and detecting whether the first voice signal is a sound of a specified user or not through a first voiceprint recognition model; and when the first voice signal is the voice signalof the specified user, and the sound of the specified type exists in the first voice signal, transmitting prompt information to a second terminal. According to the specified crowd monitoring method and device, a prompt operation is carried out only when the first voice signal of the specified type of the specified user is detected, so that false operations caused by the detection of a voice signal of a type specified by others is avoided, and the monitoring accuracy is improved.