41 results about "Pedestrian detection" patented technology

The various embodiments described herein include methods, devices, and systems for providing event alerts. In one aspect, a method includes obtaining a video feed. A frame of the video feed is analyzed at a first resolution to determine whether the frame includes a potential instance of a person. In accordance with the determination that the image includes the potential instance, a region is denoted around the potential instance. The region is analyzed at a second resolution, greater than the first resolution. In accordance with a determination that the region includes the instance of the person. a determination that the frame includes the person is made. An indication of the determination is stored for use in subsequent alert notification processing.

A work vehicle includes an operator seat and a seat sensor configured to provide a first signal in response to presence of an operator seated in the operator seat. A pedestrian detection system includes an electronic controller operatively coupled with one or more sensors. The pedestrian detection system is configured to detect presence of a pedestrian in proximity to the work vehicle. A controllable switch device operatively coupled with the seat sensor and the pedestrian detection system. The controllable switch device is configured to provide a second signal to activate the pedestrian detection system in response to the first signal provided by the seat sensor.

This invention discloses a scale-redistribution-based top-view fisheye pedestrian detection method, belonging to the field of target detection technology. In the feature generation stage, this invention employs frequency-preserving downsampling, concatenating low-frequency contours and high-frequency textures after sub-pixel rearrangement and wavelet decomposition in the channel to achieve spatial depth swapping. In the feature fusion stage, it increases the participation ratio of high-resolution features through adaptive upsampling for geometric alignment. In the spatial computation stage, it divides features into high-response and low-response regions by learning a spatial weight map, performing focused convolution only on high-response regions. In the optimization stage, it constructs a joint regression mechanism, jointly modeling overlap consistency and distribution distance, adjusting sample gradient contributions based on detection difficulty, and controlling the angle supervision intensity according to the target aspect ratio, forming a scale redistribution that spans multiple stages. This invention achieves stable detection of small-scale and distorted targets in top-view fisheye images, improving the detection consistency between edge and dense regions.

A pedestrian detection device for blind spots on buses with risk avoidance function includes a powered telescopic frame, a pedestrian recognition module, an audible and visual alarm, an environmental recognition module, a geographic location recognition module, an emergency avoidance module, and an embedded microcontroller. The powered telescopic frame is installed on the front roof of the bus. The pedestrian recognition module, environmental recognition module, and emergency avoidance module are all mounted on the powered telescopic frame. The audible and visual alarm and the embedded microcontroller are both installed on the interior ceiling of the bus, directly below the powered telescopic frame. The geographic location recognition module is mounted on the embedded microcontroller. The embedded microcontroller is connected to the powered telescopic frame, pedestrian recognition module, audible and visual alarm, environmental recognition module, geographic location recognition module, and emergency avoidance module via signals. This invention can effectively identify pedestrians in the blind spots of buses, alert the driver to driving safety through the audible and visual alarm to avoid traffic accidents, and also has risk avoidance and emergency avoidance functions.

This invention discloses a pedestrian detection method based on improved YOLOv8 using infrared images in dark environments. This method improves YOLOv8 by constructing a C2fFastD module and adding an SE channel attention layer. It utilizes an infrared camera to acquire real-time infrared images of pedestrians and detects pedestrian targets in dark environments. To verify the feasibility of the proposed method, comparative experiments were conducted with classic target detection models. Experimental results show improvements in accuracy, false negative rate, and detection speed.

This invention provides a bidirectional feedback collaborative optimization method, medium, and device for pedestrian detection and tracking. The method includes: generating an initial set of detection boxes using a detector for the current frame image; generating virtual detection boxes using trajectory-guided detection completion; obtaining the detection box set after verification; and employing bidirectional feedback collaborative optimization of the detector and tracker, including: feeding the virtual detection boxes as positive hard examples back to the detector for optimization; feeding back high-confidence real detection boxes from the detection box set to the tracker for tracker updates; and performing a second data association using the verified detection box set and the tracker optimized by bidirectional feedback, ultimately outputting a set of pedestrian trajectories. This method can solve the problems of error accumulation, false detection propagation, and trajectory fragmentation caused by the unidirectional serial interaction between the detector and tracker in existing MOT systems.

The application belongs to the technical field of deep learning, and particularly relates to a pedestrian detection and re-identification method, system and terminal based on deep learning, which comprises the following steps: S1, data preprocessing: arranging and dividing pedestrian images according to IDs into a training set and a test set, and the images in the sets are not repeated; S2, constructing a pedestrian re-identification model: using a deep learning framework PyTorch to build an L2GNet model, which is composed of 17 convolution layers in total, and each convolution uses ReLU as an activation function; S3, model training: training the L2GNet on the training set, and iteratively updating the parameters of the network according to the back propagation principle; S4, inputting the test set data into the trained model to perform a detection and re-identification task, calculating a pedestrian re-identification performance index, and saving the queried pedestrian images. The application builds a pedestrian detection and re-identification model, and the trained model can be used for pedestrian detection and re-identification tasks.

The application discloses a pedestrian cross-border head tracking method and system based on multi-modal dynamic feature fusion, belongs to the technical field of computer vision, and aims to solve the technical problem of how to improve the accuracy and efficiency of pedestrian detection and the stability of cross-border head tracking. By adopting advanced backbone networks, an adaptive anchor box mechanism and cross-scale feature fusion technology, the detection capability for small targets and high occlusion rate pedestrian targets is improved. Intelligent caching strategies and model fusion methods reduce the repeated calculation of instance segmentation and improve the efficiency of dynamic pedestrian segmentation. The combination of various feature extraction and enhancement technologies, including optimized Mamba, Transformer and multi-granularity feature fusion, enhances the purity, discriminability and adaptability to occlusion and view angle changes of features.

The application discloses a pedestrian target detection method based on radar video infrared decision-level fusion, and relates to the technical field of automatic driving perception. The application aims to solve the problem that the existing pedestrian detection method uses a single mode of video sensor to perform a pedestrian target detection task under poor light conditions, and the intelligent driving vehicle has more false detection and missed detection conditions for pedestrian detection, so that the intelligent driving vehicle depending on ADAS cannot timely avoid pedestrians and makes an incorrect driving strategy and even a collision occurs. The process is as follows: a pedestrian detection platform is built; laser radar, visible light cameras and infrared cameras are time and space aligned; pedestrian targets in laser radar point clouds, pedestrian targets in visible light images and pedestrian targets in infrared images are detected; target detection results are fused based on a decision-level fusion algorithm to obtain final pedestrian target detection results.

This invention provides a dynamic water pressure control method for intelligent sprinkler trucks based on MPC prediction, relating to the fields of wireless communication and intelligent control. The method includes the following steps: S1, data acquisition and preprocessing; S2, pedestrian detection and tracking; S3, generating an optimal avoidance trajectory based on MPC; S4, constructing a multi-condition adaptive water pressure model to regulate the water pressure value; S5, using a PID algorithm for closed-loop water pressure control; S6, switching the sprinkler truck's operating mode according to actual road conditions. Beneficial effects: The method employs multi-sensor perception combined with deep learning and integrated tracking algorithms to achieve accurate acquisition of pedestrian dynamic information; the trajectory planning prediction mechanism using model predictive control upgrades passive response to proactive prevention, reducing safety hazards caused by operational errors; the water pressure model is constructed, and closed-loop control enables real-time adjustment; adjusting the optimization coefficients according to the scenario improves the method's applicability to different scenarios.

A work vehicle includes an operator seat and a seat sensor configured to provide a first signal in response to presence of an operator seated in the operator seat. A pedestrian detection system includes an electronic controller operatively coupled with one or more sensors. The pedestrian detection system is configured to detect presence of a pedestrian in proximity to the work vehicle. A controllable switch device operatively coupled with the seat sensor and the pedestrian detection system. The controllable switch device is configured to provide a second signal to activate the pedestrian detection system in response to the first signal provided by the seat sensor.

This invention relates to the field of pedestrian trajectory prediction technology and discloses a pedestrian detection and trajectory prediction method based on a spatiotemporal joint attention mechanism. The method includes acquiring video images of the quay crane operation area and synchronous mechanical status data; projecting the mechanical status data onto an image plane to generate a dynamic occlusion mask; constructing an attention decay weight field based on the dynamic occlusion mask; using the attention decay weight field to correct the query and key feature similarity calculation of the spatiotemporal attention mechanism; and performing pedestrian bounding box detection and future trajectory sequence prediction based on the corrected spatiotemporal attention features. This invention, by introducing the mechanical motion parameters of the quay crane PLC to construct an occlusion thermal mask and guide the spatiotemporal attention calculation, can force the model to focus on the effective passable area under quay crane occlusion conditions.

The application provides a pedestrian detection method and system based on severe weather, equipment and medium, belonging to the technical field of data processing. The method comprises: acquiring a modulated light field sequence and a corresponding light intensity signal sequence; the modulated light field sequence is generated according to a plurality of sequentially arranged speckle patterns, the modulated light field sequence comprises a plurality of modulation patterns, and the light intensity signal sequence is time-synchronized with the modulated light field sequence; inputting the modulation patterns of the modulated light field sequence and the light intensity signal sequence into a target detection model, performing feature encoding and correlation calculation on the two sequences through a backbone network of the target detection model to obtain a correlation feature map representing light field correlation information; and obtaining a pedestrian detection result in the target area based on the target detection model and the correlation feature map. The application improves the efficiency and accuracy of pedestrian detection in severe weather.

The application provides a kind of people flow detection method and equipment, including obtaining the real-time video data of people flow detection area;Using trained detection model, real-time video data is processed, and the pedestrian detection frame of current frame in real-time video data is obtained;Pedestrian detection frame data of current frame is input into target tracker, and the pedestrian prediction frame of future frame is obtained, and pedestrian detection frame and pedestrian prediction frame are matched, and each pedestrian detection frame is assigned identification number according to matching result;According to the detection line of people flow detection direction, the people flow statistical result is obtained according to the identification number of pedestrian detection frame passing through detection line.Through the above setting, high-precision, high real-time people flow detection can be realized with lower power consumption.

A real-time multimodal pedestrian image detection method and system is disclosed. This invention relates to the fields of intelligent transportation systems, autonomous driving environmental perception, and computer vision, specifically a real-time multimodal pedestrian image detection method and system. The invention constructs a MAFD-DETR detection model: In the visible light branch, an adaptive frequency-domain gated wavelet convolution is introduced. By constructing a global frequency-domain gated unit, frequency band weights are dynamically learned to adaptively suppress high-frequency noise and enhance texture features. In the infrared branch, a thermal gradient-guided windmill-shaped convolution is introduced. Central difference convolution is used to extract edge temperature gradients, and gradient masks are used to modulate the windmill-shaped intensity features, effectively filtering background thermal noise. Finally, a progressive semantic fusion module integrates dual-stream features and outputs the detection results. This invention significantly improves the accuracy and robustness of pedestrian detection in all-weather scenarios and meets the real-time requirements of in-vehicle systems.

A method for detecting pedestrians by improving RT-DETR, comprising the following steps: step 1: selecting a public pedestrian detection dataset Widerperson, then cleaning and retaining suitable parts; step 2: image enhancement and preprocessing are performed on the image data, and the training set, the validation set and the test set are divided according to the preset proportion; step 3: an enhanced real-time target detection model iRT-DETR is constructed, the model comprising a stripe convolution channel gating module (SCCG) in a backbone network, an adaptive scale decomposition attention module (ASDA) in an encoder, and a bidirectional multi-assistant feature pyramid network (BiMA-FPN); step 4: the training set obtained in step 2 is input into the model constructed in step 3 for training, and the final training convergent detection model is obtained through loss calculation, back propagation and hyperparameter adjustment; step 5: the trained model is used to detect input pedestrian images, and the class and boundary box position of the pedestrian target are output.

The present application relates to a kind of single-spectrum night pedestrian detection method and system based on foreground-background contrast attention.The method comprises: the image to be detected is input into the backbone network to extract different scale features containing different semantic information and detail information;Different scale features extracted by the backbone network are input into the feature fusion network, the different scale features are fused by the feature fusion network, and the feature in the foreground feature and background information two dimensions is adaptively adjusted by foreground-background contrast attention, i.e.strengthen the attention degree of foreground feature, weaken the attention degree of background information, so that the network can focus more on foreground feature in night scene;The multi-scale features after fusion are sent to the detection head, and the detection head maps the multi-scale features after fusion into a prediction box, so as to obtain pedestrian detection result.The present application can effectively improve the accuracy of night pedestrian detection.

A work vehicle includes an operator seat and a seat sensor configured to provide a first signal in response to presence of an operator seated in the operator seat. A pedestrian detection system includes an electronic controller operatively coupled with one or more sensors. The pedestrian detection system is configured to detect presence of a pedestrian in proximity to the work vehicle. A controllable switch device operatively coupled with the seat sensor and the pedestrian detection system. The controllable switch device is configured to provide a second signal to activate the pedestrian detection system in response to the first signal provided by the seat sensor.

The present application provides a kind of night pedestrian detection method, the present application is based on the network structure of existing YOLOv5 algorithm to improve part details, make network have better detection performance, further guarantee the precision and speed of fusion algorithm.The present application carries out pre-processing to radar initial data, reduces the interference of invalid target to radar data, improves algorithm running speed, uses unscented Kalman filter algorithm to detect and track radar data, reduces radar detection false alarm rate, improves the reliability and stability of radar data.For the problem that single sensor cannot accurately obtain the comprehensive state information of night pedestrian target, the present application makes full use of the performance advantages of infrared camera and millimeter wave radar, avoids the missed detection and false detection of single sensor to target detection pedestrian etc.Using fusion algorithm can quickly and accurately detect pedestrian target in different scenes at night.

An image processing device of a person detection system mounted on a moving body is configured to: detect, in image data obtained from a camera, an area in which an obstacle appears; determine whether the area meets an upper body detection process condition that the obstacle in the area is distanced from a road surface within a predetermined range from the camera; perform an upper body detection process in which the area of the image data is compared with upper body comparison data to determine whether the obstacle in the area is a person, for the area that meets the upper body detection process condition; and perform a whole-body detection process in which the area of the image data is compared with whole-body comparison data to determine whether the obstacle in the area is a person, for the area that does not meet the upper body detection process condition.

This invention provides an online vehicle and pedestrian detection and tracking method based on an improved ByteTrack, belonging to the field of computer vision technology. This invention integrates target detection, motion state estimation, appearance feature extraction, data association algorithms, and post-processing trajectory interpolation methods to optimize and improve the MOT algorithm model ByteTrack. It utilizes model compression acceleration tools PocketFlow, TVM, and TensorRT to achieve lightweight, real-time, and efficient performance. The system covers functions such as single-camera tracking, multi-class tracking, trajectory drawing, and pedestrian / vehicle traffic statistics. In the UI, users can selectively choose the improved and optimized ByteTrack model and other comparative model parameters to detect and track targets. The target sources for detection, tracking, and counting can be video and image files, or real-time online processing of images captured by connected cameras. It has significant practical application and development potential in scenarios such as vehicle behavior analysis, traffic violation identification, and suspect tracking.

The application discloses a dense pedestrian detection method based on adaptive perception YOLOv8, and comprises the following steps: step 1, acquiring and processing a dense pedestrian detection dataset, and performing division and data enhancement processing on the dataset; step 2, improving an existing YOLOv8 model to obtain an AP-YOLOv8 model; and step 3, training the AP-YOLOv8 model, and using the trained AP-YOLOv8 model to perform dense pedestrian detection. The method can improve the detection precision of the existing YOLOv8 model in a dense pedestrian scene.

The present invention discloses a monitoring device for coal mine track transportation, which relates to the technical field of monitoring devices and includes a bottom plate. One side of the bottom plate is fixedly provided with a fixed rod, one side of the fixed rod is slidably provided with a clamping plate, the top of the fixed rod is fixedly provided with a lifting device, and the top of the lifting device is fixedly provided with a fixed box. The present invention uses a lifting device to raise or lower the monitor, and uses a motor to control the rotation angle of the monitor. In order to prevent the various transportation devices and pedestrian detection devices from existing separately in the existing mine track transportation without state monitoring functions and avoid potential safety hazards during the progress of the mine car, a monitor is used for monitoring to ensure transportation safety. And in order to conduct comprehensive monitoring, a motor is used to control the rotation angle of the monitor, rotate the disc on the rotating shaft, pull out the insertion rod and insert the insertion rod into the slot to fix the monitor, ensuring that all aspects of the monitor can be monitored.

The application discloses a visual language model guided indoor fire pedestrian detection and re-identification system and belongs to the technical field of computer vision and artificial intelligence. In view of the problems of instability in complex scenes, discontinuity in identification, lack of semantic guidance mechanism and cross-frame semantic consistency of the existing indoor fire pedestrian detection system, the application encodes the pedestrian-related text description through a text feature generation module, extracts multi-scale visual features through a visual feature extraction module, generates a semantic guidance weight matrix through a text and image feature fusion module, enhances the visual features and outputs a detection result through a semantic guidance detection module, realizes cross-frame identity consistency maintenance through a semantic guidance pedestrian re-identification module, and dynamically updates a pedestrian feature library through an online learning module. The system improves the detection precision and robustness under the environment of smoke, uneven illumination and flame interference, and enhances the continuity and stability of identity re-identification.

This application relates to the field of image processing technology, and provides a method, apparatus, computer device, and computer program product for generating simulated images. The method includes: constructing a three-dimensional scene based on pre-acquired real-world images and map data; integrating foreground materials into the three-dimensional scene; rendering the three-dimensional scene after integrating the foreground materials; and generating a simulated image for target detection. This simulated image can accurately simulate various scenes in the real environment, thereby helping pedestrian detection or recognition systems to be more accurate when processing actual images.

This application provides a method for training a detection model, a pedestrian detection method, an apparatus, and related equipment, belonging to the field of computer vision technology. The method includes: acquiring sample pedestrian images and sample background images that match the scene in the sample pedestrian images; inputting the sample pedestrian images and sample background images into an initial detection model; extracting features from the background in the sample background images to obtain initial background features, and extracting features from the pedestrians in the sample pedestrian images to obtain initial pedestrian features; fusing the initial background features and initial pedestrian features to obtain sample fusion features; predicting the background in the sample fusion features to obtain sample background features; calculating a background loss value based on the sample background features and initial background features; training the initial detection model based on the background loss value to obtain the trained detection model. This application can improve the accuracy of pedestrian detection.

This application provides an image display method and a vehicle. The method includes: acquiring a current frame night vision image captured by a night vision camera unit; determining at least one image region to be tested from the current frame night vision image; extracting image feature vectors of each image region to be tested and inputting them into a pre-trained classifier to obtain a first classification result indicating whether a pedestrian exists in the image region to be tested; if no first classification result is obtained, extracting image sub-feature vectors of each image sub-region in the image region to be tested and determining a second classification result for the image region to be tested; based on the first or second classification result corresponding to each image region to be tested, marking the regions where pedestrians are determined to exist in the current frame night vision image to obtain a target night vision image; and displaying the target night vision image in a display unit. The technical solution provided by the embodiments of this application can improve the reliability of pedestrian detection in night vision images.