3197 results about "Pedestrian" patented technology

The invention relates to a multi-stage filtering road spilled object detection method based on dynamic difference analysis, which is suitable for automatic identification of unstructured foreign matters in video monitoring. The method comprises the following steps: firstly, extracting a reference image road mask, eliminating vehicle and pedestrian interference by using YOLOv8 detection, and extracting a motion candidate area through a frame difference method and background modeling; and then context expansion and super-resolution reconstruction are carried out on the candidate region, the candidate region is converted into an HSV space, multi-dimensional features such as color similarity, structural similarity and shadow determination are synthesized for screening, false detection is further removed in combination with inter-frame time sequence consistency, and finally a stable detection result is output. The method provided by the invention has the advantages of strong anti-interference capability, high adaptability, high detection precision and the like, and is suitable for the intelligent recognition task of the expressway thrown objects in a complex environment.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention discloses a long-time pedestrian re-identification method based on dual-path cooperation and key frame guided reconstruction. The method comprises the steps of firstly collecting a pedestrian video to be recognized, and extracting a video feature sequence; space and time position coding is introduced into the video feature sequence; capturing local fine-grained dynamic features through a local dynamic feature capturing path, and modeling long-range time sequence association through a cross-frame global feature modeling path; then, dual-path feature complementation is realized through bidirectional gating interaction; further screening out key frames, and realizing feature reconstruction through a full-frame attention propagation mechanism; and finally fusing the dual-path fusion features, the key frame guide reconstruction features and the refined features to generate pedestrian identity features. And processing pedestrian identity features to obtain standardized feature vectors, performing similarity comparison on the standardized feature vectors and pedestrian features in an image library, and returning a matching list. According to the method, video time sequence information is fully utilized, and the problem of insufficient robustness caused by appearance change in long-time pedestrian re-identification is effectively solved.

The invention discloses a target detection tracking method and device based on radar visual fusion perception, and a medium, and the method employs visual detection as a leading part to establish a radar visual fusion tracking module, and solves a problem that the detection precision, tracking stability and environment robustness are difficult to give consideration to the existing single-mode perception in a complex traffic environment at the same time. Through unified multi-modal fusion of distance and speed information of visual detection, visual tracking and millimeter wave radar, stable, continuous and reliable target identification and track output of targets such as pedestrians and vehicles under a low-computing-power platform are realized.

The invention provides a robot social adaptive navigation knowledge learning and migration method and system, and relates to the field of mobile robot navigation. Aiming at the problems that an existing path planner lacks time sequence memory and neglects pedestrian social intent, a man-machine co-fusion scene is constructed, a training set containing an expert demonstration path is made, and a recursive generation model is input; designing a recurrent neural network embedded RRT, generating an RNN-RRT planner, and fusing historical information and pedestrian convergence probability in training; new scene loading training parameters are finely adjusted to realize knowledge migration, loss convergence or output RNN final parameters after reaching a preset round number. According to the method, the path anthropomorphism and generalization ability are improved, and the method is suitable for complex human-computer interaction scenes.

The invention relates to the technical field of pedestrian floor tile monitoring, and discloses a pedestrian floor tile road condition analysis method based on a knowledge graph. The method comprises the following steps: firstly, acquiring spatial topological data and historical maintenance records of road infrastructures, fusing real-time road condition monitoring data and environmental parameters acquired by a multi-source sensor, and constructing a pedestrian floor tile state knowledge graph structure; analyzing a data association relationship through a map entity alignment algorithm to generate road condition evaluation features, and updating map node weights by using a dynamic path optimization algorithm in combination with real-time data and environmental parameters; then, based on the updated node weight and evaluation features, an intelligent agent decision engine is operated to output a floor tile area risk evaluation result; and finally, collecting whole-process data, and optimizing a knowledge graph structure by using an incremental graph updating algorithm. According to the method, multi-source data fusion and dynamic analysis are realized, and delicacy management of pedestrian floor tile road conditions is assisted.

An operating method of a first device (100) in a wireless communication system is presented. The method may comprise the steps of: determining that a first device (100) interrupts walking of a pedestrian; and determining, on the basis of the determination that the first device (100) interrupts walking of the pedestrian, whether to perform a first operation for preventing the interruption.

The invention discloses a DeepSORT pedestrian tracking method based on multi-feature space-time cooperative interaction, and belongs to the field of computer vision and intelligent video analysis. The method comprises the following steps: acquiring and processing pedestrian data, constructing a target detection and feature extraction model, detecting a test set after training to generate a candidate box, extracting appearance features to construct a cost matrix, matching and updating a trajectory by using a Hungary algorithm, and finally outputting a visual tracking result. In the detection stage, a small target feature enhancement pyramid is designed to improve the small target detection precision, PSConv, Triplet Attention and DyHead are fused to construct a multi-dimensional feature interaction mechanism, and the scale adaptability and the anti-shielding capability are enhanced; in the tracking stage, an IAU module is embedded into an Re-ID branch of DeepSORT, feature discrimination is enhanced through space-time and channel feature dynamic modeling, and ID Switch is reduced. The method effectively improves the perception recognition capability of a multi-scale and strong-shielding target, guarantees the detection accuracy and tracking robustness in a complex environment, and has a good application deployment value.

The invention discloses an occluded pedestrian re-identification method based on cross-layer frequency domain enhancement and multi-view fusion, and the method comprises the steps: employing ResNet-50 as a backbone network, inputting a plurality of pedestrian images of the same identity according to groups, obtaining multiple layers, carrying out the frequency separation and frequency enhancement, forming a significant mask which is more sensitive to the occlusion and background, and carrying out the recognition of the pedestrian images. And meanwhile, the lay1 obtains global features with the same scale as the lay4 through GCSA, and weighted fusion is carried out on the global features and the lay4 features according to the occlusion score, so that the occluded area is completed. Group-level representation with multi-view attention fusion, output information complementation and noise suppression is adopted. In the training stage, the identity classification loss of each branch and the cross-branch consistency loss are jointly optimized, and gradient cutting is matched to improve the stability. According to the method, on the premise that a ResNet-50 backbone structure is not changed, cross-layer frequency domain prior is used for accurate shielding positioning, global-local adaptive fusion based on shielding scores and multi-view weighting are combined, and the pedestrian re-recognition precision and robustness in a shielding scene are remarkably improved.

The invention relates to the technical field of pedestrian re-identification, and particularly provides an end-to-end cross-modal pedestrian re-identification method based on multi-domain feature alignment. The method comprises the following steps: splitting original text description into two sub-descriptions of identity description and clothing description through a text description separation module, and providing structured semantic input for cross-modal feature alignment; according to the two sub-descriptions of the identity and the clothing, the identity-clothing bidirectional decoupling alignment module utilizes an attention mechanism and a gating weighting strategy to realize cross-modal feature alignment; based on cross-modal feature alignment, introducing a Mama state space model SSM into a cross-modal pedestrian re-identification ReID task, and fusing image and text features; according to the features of the fused image and the text, a multi-target robust optimization module is designed for optimization, and a final retrieval result is output, the precision of fine-grained semantic alignment is improved, effective context collaboration is achieved, and balance between discrimination and robustness is achieved.

The invention discloses a method for realizing holographic traffic at an intersection. The method comprises the following steps: acquiring a real-time video stream at the intersection; motor vehicles, non-motor vehicles and pedestrians in the video are identified based on a deep learning target detection algorithm, a continuous motion track of each target is generated through a multi-target tracking algorithm, identity association is carried out on cross-camera targets in combination with a re-identification algorithm, and structured traffic data including target types, real-time speeds and motion directions are output; obtaining coordinate points of each lane of the intersection based on a geographic information system, and constructing a lane network topological relation; combining with the structured traffic data to generate target trajectory data; and constructing an intersection three-dimensional model based on an unreal engine, generating a virtual target in the three-dimensional model according to the target trajectory data, and driving the virtual target to move in real time. According to the method, a holographic traffic implementation scheme is provided for a business scene which is free of radar and only monitored by a video.

The invention belongs to the field of visual tracking, and relates to a multi-target visual tracking method based on cooperation of a dynamic neural field and a neural network, which takes a cross-modal cooperation architecture as a core and comprises a dynamic neural field module based on multi-target trajectory maintenance and shielding matching and an improved MoESDQ neural network module. Meanwhile, a collaborative decision-making mechanism is designed, when the activation peak value of the dynamic neural field is attenuated to a preset threshold value, neural network feature matching is triggered, and disappearance target reproduction correlation is achieved based on cosine similarity. The objective of the invention is to solve the visual tracking capability under the condition of scene and target motion change in a monitoring range, for example, under an intelligent traffic intersection scene. The problems of high ID switching rate, multi-target misassociation and low tracking precision under a real-time tracking background caused by scene change or frequent shielding of vehicles and pedestrians, similar target appearances, transient disappearance and reproduction of the targets and sudden illumination change are solved.

The invention provides a mobile robot autonomous navigation method based on pedestrian trajectory prediction obstacle avoidance, which comprises the following steps: firstly, initializing a robot H, and acquiring 3D laser radar information, camera information, local map data and pose information of the robot H; secondly, according to the position information of the robot H, the multi-modal fusion information and the local map data, the dynamic obstacle state and the static obstacle position are obtained, the types of obstacles are distinguished, and passable areas are divided; next, a prediction trajectory is generated by using a progressive learning trajectory prediction network of LSTM + GAN, sub-target points are generated in a passable area in combination with an RRT algorithm, an optimal sub-target point is selected through an evaluation function, and global path planning is performed by using BIRRT; and finally, inputting the predicted trajectory into a DWA algorithm to realize local path planning and real-time obstacle avoidance of the robot. The obstacle avoidance capability and navigation efficiency of the robot are remarkably improved, the detouring distance and time are reduced, and the adaptability and flexibility of the robot in logistics storage and other scenes are enhanced.

The invention discloses an unmanned aerial vehicle intelligent obstacle avoidance decision-making method and system for a complex environment, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance. The method comprises the steps of S1, environment perception and data acquisition, S2, multi-source data hierarchical fusion processing, S3, complex environment obstacle recognition and risk assessment, S4, dynamic obstacle avoidance path decision and optimization, and S5, obstacle avoidance execution and real-time adjustment. Omnibearing acquisition of obstacle distance, environment images, unmanned aerial vehicle attitude and position data is realized; the method combines an improved YOLOv8 model (adding a CBAM attention mechanism), strengthens the obstacle feature extraction capability under complex weathers such as rain and fog, strong light and dust, can accurately distinguish static obstacles such as buildings and trees from dynamic obstacles such as pedestrians, vehicles and other aircrafts, and solves the problems that a single sensor perceives blind areas and is insufficient in precision in a complex environment.

The invention discloses a sidewalk damage detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, a DS-HAF module is introduced into a model neck network, and multi-scale feature fusion, channel-space joint attention enhancement and bidirectional residual error guided deep and shallow feature dynamic enhancement fusion are performed, so that the collaborative effect of detail and semantic expression is optimized, and complex background interference is effectively inhibited. An MAGRDet detection head is introduced into the detection network, multi-branch feature extraction and alignment fusion are realized through an MAGR module, and the detection capability of small targets, low-contrast targets and shielded crack targets is improved through a channel-space cascade attention modulation fusion result. And meanwhile, an MDPAR module is introduced into the backbone network, so that efficient and robust cross-scale feature extraction is realized, and the perception capability and the anti-interference performance of the model on multi-scale sidewalk cracks and damages are improved while low calculation overhead is maintained.

The invention relates to the technical field of traffic management, in particular to an urban road intersection passage intelligent optimization method based on multi-modal information, which comprises the following steps: acquiring real-time sensing data of pedestrians and non-motor vehicles through a video camera, a millimeter wave radar and a laser radar, and generating fusion sensing data through timestamp synchronization and coordinate system unification; identifying and generating a target list with category labels by using a detection and clustering algorithm, and obtaining a stable motion trail and intensity by combining with multi-target tracking; predicting a crossing intention and a path based on time sequence deep learning, calculating an interleaving point and quantifying a conflict risk; and according to a comparison result of the conflict risk coefficient and a threshold value, generating a strategy control instruction of different time periods, different paths or a mixed mode, and in combination with execution time window information, forming an optimized timing scheme through cooperative execution of an intelligent prompt identifier, a telescopic isolation belt and a signal control machine, so as to realize cooperative passage. The method improves the recognition precision, reduces the conflict risk, and improves the passing efficiency.

The invention provides a dynamic planning method for a fire evacuation path of a deep subway station based on a cellular automaton, and the method comprises the steps: building a subway space topology through employing the cellular automaton, simulating a fire environment through employing an FDS tool, and obtaining environment data; constructing a multi-field model based on subway space topology and environment data; based on the multi-field model, the pedestrian transition probability is calculated, then the pedestrian transition probability is corrected in combination with the deep subway facility characteristics and the pedestrian consensus behaviors, and finally the corrected pedestrian transition probability is output; and optimizing a cost function of an A star algorithm based on the corrected pedestrian transition probability to obtain a dynamic cost function, further obtaining an improved A star algorithm, planning an initial path based on the improved A star algorithm, formulating a path updating rule, and implementing a smooth transition strategy to realize dynamic path planning. According to the invention, the evacuation safety and the scheduling efficiency under the fire situation can be improved.

The invention discloses a pedestrian re-identification method and system based on cross-modal feature fusion and a medium, and the method comprises the steps: carrying out the human body contour detection of a to-be-detected video stream obtained in real time, and obtaining a first continuous frame set and a second continuous frame set; respectively inputting the first continuous frame set and the second continuous frame set into a preset target recognition model so as to extract target gait features from the first continuous frame set through a first extraction branch and extract appearance features and clothes features from the second continuous frame set through a second extraction branch, mapping the target gait feature to a semantic subspace to obtain a gait identity vector, and performing feature fusion on the gait identity vector and the appearance feature to obtain a target feature vector; and performing similarity matching on the target feature vector to determine a pedestrian re-identification result. According to the invention, the accuracy and robustness of pedestrian re-identification can be improved.

The embodiment of the invention provides a robot activity area division method and device, a robot, a storage medium and a program product. The method comprises the steps of obtaining kinematics data of the robot; and calculating a core braking area of the robot according to the kinematics data of the robot. And acquiring external environment information and system response time. And according to the external environment information and the system response time, performing activity area expansion on the core braking area, and calculating an early warning area. The robot movement area is determined according to the core braking area and the early warning area of the robot, and the safety of the robot and pedestrians is improved.

The invention provides a pedestrian and vehicle detection method and device based on multi-scale perception fusion, and the method comprises the steps: building a multi-scale perception fusion network structure which comprises an SPDConv module, a PSSF module, an FCPAM module and a DyHead module, solving a detection error caused by the problems of target shielding, size change, far-small proportion and the like in a complex traffic environment, and improving the detection precision. And the stability, the precision and the deployability of the detection model are improved.

The invention relates to an intelligent vehicle taillight system, a control method thereof and a vehicle, the intelligent vehicle taillight system comprises a data acquisition module used for acquiring vehicle dynamic operation parameters and external environment information, the vehicle dynamic operation parameters comprise at least one of brake pedal stroke, steering angle, vehicle speed and automatic driving state data, and the external environment information is used for acquiring the external environment information; the external environment information comprises at least one of a rear vehicle distance and a pedestrian position; the intelligent control module is connected with the data acquisition module and is used for generating current working condition information of the vehicle based on the vehicle dynamic operation parameters and the external environment information; and the tail lamp is connected with the intelligent control module and performs corresponding display and projection operation according to the current working condition information. According to the invention, the driving safety can be improved.

The invention relates to the field of pollutant monitoring, in particular to an indoor decoration harmful substance monitoring and early warning method and system based on multi-sensor analysis. The method comprises the following steps that decoration whole-process parameter collection is carried out based on multiple sensor nodes, regional parameter distribution fitting is carried out, and an all-weather monitoring reference database is constructed; performing air pollutant identification marking on the all-weather monitoring reference database, and performing multi-time-point concentration change evolution analysis to obtain a multi-time-point concentration distribution evolution model; carrying out dynamic pollutant diffusion mining on the multi-time-point concentration distribution evolution model, carrying out combined pollution evaluation calculation, and constructing a multi-pollutant combined diffusion risk chain; dynamic interpolation modeling is carried out on the multi-time-point concentration distribution evolution model, micro-scale space-time disturbance reconstruction is carried out, and a pedestrian disturbance space-time diffusion model is constructed. According to the invention, through accurate and efficient risk assessment of indoor decoration harmful substances, dynamic early warning is carried out, and the safety of indoor decoration is improved.

According to the target tracking and identification method, the monitoring device and the storage medium disclosed by the invention, the image frames are continuously acquired from the monitoring video source by extracting the appearance characteristics of the specified main target in the current image frame, and the position of each pedestrian target in the image frames is detected in real time; matching the identity label and the motion trail of each pedestrian target in the current image frame; if the identity identifier of the main target is matched in the current image frame, performing similarity judgment on the historical motion trail of the main target and the motion trails of other pedestrian targets in the current image frame, and recording; and if the number of times of similarity in the plurality of continuous frames is greater than a threshold value, querying the final visible position of the main target, screening the main target according to the final visible position, and expanding the detection area step by step until the main target is screened or the whole image is covered when the main target is not screened. Therefore, the target identity abnormity is effectively recognized, the misrecognition risk is avoided, and the recovery efficiency and accuracy are improved.

The invention relates to a human traffic monitoring method and system for a dense sitting posture scene, and the method comprises the steps: S1, obtaining the video stream data of the dense sitting posture scene, constructing an original data set, and optimizing the size distribution of an anchor frame; s2, constructing a human traffic monitoring model which is improved and realized on the basis of a YOLOv7 model: in a backbone network, replacing the four starting CBS modules with ODSConv modules, and replacing the two subsequent ELAN modules with E-ELAN-Sim modules; in the head network, a coordinate attention mechanism is embedded in an SPPCSPC module to obtain a CA-SPPCSPC module, the CA module is added in front of a CBS module connected with an ELAN-H module and a Concat module, and meanwhile, a four-scale detection framework is realized in the head network to enhance the small target detection capability; training the constructed people flow supervision model through the training data set; and S3, inputting real-time video stream data into the trained people flow supervision model, and outputting a people flow supervision quantity. According to the method and the system, accurate detection and real-time statistics of the sitting posture target person in the dense sitting posture scene can be realized.

The invention discloses a side slope landslide monitoring method, system and device and a storage medium. The monitoring method comprises the steps that the risk level of each sub-region is determined according to a deformation monitoring result in a side slope monitoring region; according to the risk level, adaptively adjusting a threshold value in a CFAR detection algorithm; wherein the higher the risk level is, the lower the adjusted threshold value is; and carrying out moving target detection on the slope monitoring area based on the adjusted threshold value. According to the method, the deformation and the moving target are monitored at the same time through the single radar, the deformation monitoring result is fused into the moving target detection process, the threshold value in the CFAR detection algorithm is adjusted in a self-adaptive mode according to the risk level of each sub-region, the capturing capacity of the moving target is improved, and meanwhile false alarms caused by interference of pedestrians, animals and the like are effectively restrained.

The present application relates to the field of intelligent driving. Provided are a resource scheduling method and apparatus, and a system. The method comprises: running a first application program, wherein the first application program comprises a first critical data stream, the first critical data stream is used for determining the output of the first application program, the first critical data stream is associated with N threads under M processes, the M processes comprise a first process, the first process is associated with a first thread, a first function and a first stub, and the first thread is used for running the first function, M and N being positive integers; and on the basis of the first stub, allocating a first computing resource to the first thread, wherein the first computing resource is used for the first thread to run the first function. On the basis of the method, computing resources can be reasonably allocated to threads under application nodes corresponding to different critical data streams in autonomous driving applications, thereby helping to reduce the end-to-end latency and latency jitter of critical data streams in autonomous driving systems, and further ensuring the safety of vehicle owners and pedestrians outside vehicles.

A foot bridge structure comprises a plurality of fish skeletons which are mutually spliced and a bridge body which is formed by splicing the fish skeletons, each fish skeleton comprises a main body beam and cross beams, the cross beams are connected to the two sides of the main body beam respectively, the cross beams are symmetrically arranged relative to the central axis of the main body beam, and a height difference exists between the top of the main body beam and the cross beams. The waterproof liquid is accumulated at the connecting position of the main body beam and the cross beam, so that the connecting position of the main body beam and the cross beam is prevented from being rusted, a bridge floor bottom plate is laid on the fish skeleton, the top of the fish skeleton is wrapped through the bridge floor bottom plate, namely, the bridge floor bottom plate is laid on the upper end faces of the main body beam and the cross beam, and therefore the liquid is stored on the bridge floor bottom plate. The liquid is further prevented from being in contact with the main body beams and the cross beams, so that the main body beams and the cross beams are prevented from being rusted, a bridge deck slab is arranged on the bridge deck bottom plate, and guardrails are arranged on the two sides of the bridge deck slab.

The invention discloses a high-precision indoor positioning system based on a smart bracelet, and the system comprises a data processing module which is used for obtaining and preprocessing multi-source sensing data collected by the smart bracelet; the gait reckoning module is used for executing gait event detection and pedestrian dead reckoning processing; the wireless observation module is used for carrying out preprocessing and credibility evaluation on indoor wireless positioning observation data; the fusion positioning module is used for carrying out credibility weighted fusion; the trajectory constraint module is used for applying trajectory rationality constraint and correcting abnormal position points; the error correction module is used for constructing a positioning error dynamic correction model and executing online correction processing; and the track output module is used for carrying out smoothing and time resampling processing and extracting a human body position determination result set. The method is based on the multi-source perception fusion and federated Kalman dynamic correction method, achieves the indoor high-precision positioning of the smart bracelet, and has the advantages of continuous track, controllable error and high environmental adaptability.

The invention discloses an intersection right-turn vehicle and pedestrian interaction trajectory prediction method and computer equipment. The method comprises the following steps: acquiring a human-vehicle historical trajectory and initial state information in intersection right-turn vehicle and pedestrian interaction; a confrontation reinforcement learning structure is improved through KL regularization, and combined learning of a reward function and a strategy is achieved; a Nash Q-learning algorithm with a KL constraint is utilized to carry out joint optimization; and inputting the optimized strategy model into a behavior simulator, driving a generated prediction trajectory by an initial state, comparing the prediction trajectory with a real trajectory, and evaluating a prediction result based on a specified index. According to the method, the performance of the model in the aspects of prediction precision, behavior consistency and environment generalization ability can be effectively improved, the stability of a reward function and a strategy network is further improved, the generalization ability of the model in different traffic scenes subsequently is enhanced, and the learned strategy has better migration potential.

The invention discloses an intelligent traffic regulation and control method and system for cities, relates to the technical field of intelligent traffic, and realizes high-precision real-time sensing of complex traffic states of urban intersections by constructing a multi-source traffic data acquisition system. A multi-modal feature fusion and semantic tag matching mechanism is adopted to recognize overlapping and dynamic changes of traffic states, a multi-strategy linkage signal timing scheme is generated in time, and the problems that a traditional signal control system is lagged in response and single in state recognition are solved. According to the method, commuting and logistics interference indexes are introduced, a release sequence adjustment and channel optimization strategy is intelligently generated according to a people flow peak and logistics concurrence situation, and the method aims at solving the problem of intersection signal timing regulation and control under the condition of complex multi-mode traffic flow superposition. And meanwhile, an execution result is collected in a closed-loop manner, a strategy deviation value is calculated in real time, error tolerance is set, adaptive iterative optimization is supported, and stable convergence of the strategy and achievement of a regulation target are ensured.