24 results about "Smoking behavior" patented technology

The invention provides a smoking behavior detection system based on an edge device, and belongs to the field of computer vision, the smoking behavior detection system is deployed on a Jetson Nano edge computing device, and the smoking behavior detection system is used for detecting the smoking behavior through video frame acquisition processing, a lightweight spatial-temporal feature extraction and recognition model and a high-speed reasoning module using a TensorRT reasoning engine. And the real-time detection of the smoking behavior in the monitoring video is realized. The system firstly preprocesses a video frame collected by a camera, and performs feature extraction on a target area and judges a smoking behavior by using a time-space behavior recognition network improved by pruning redundant Token of input data. When a smoking behavior is detected, the system may generate alarm information and alarm voice locally at the terminal. The system has the advantages of convenient deployment, high real-time performance, local reasoning and the like, and is suitable for firework safety management in various public scenes.

This invention relates to the field of indoor behavior detection technology, and discloses a method and system for detecting indoor smoking behavior based on transient change characteristics. The system includes a transient sensing module that captures multi-source sensor data streams such as temperature, humidity, air quality, and images in the target space in real time, accurately capturing smoking-related environmental changes; an environment modeling module that constructs a dynamic behavior response model based on multi-source data, transforming smoking detection into an environmental state evolution process, adaptable to complex indoor environments, and avoiding false positives and false negatives; a behavior decision module that uses a transient feature analysis algorithm as its engine to generate a change-sensing intelligent agent, identifying smoking behavior by analyzing data trends and feature combinations, reducing the false positive rate; and an execution verification module that, based on the judgment results, quickly executes intervention commands such as starting ventilation equipment and triggering alarms, achieving seamless integration of detection and intervention, reducing the harm of smoking, and providing an efficient solution for indoor smoking control.

The invention discloses a second-hand smoke monitoring and controlling device in a closed space. The device comprises a smoke source intelligent identification sub-module, and an infrared human body detection sub-module, a particulate matter concentration detection sub-module, a multimedia intervention sub-module, an air purification sub-module and an air quality announcement sub-module which are respectively in communication connection with the smoke source intelligent identification sub-module, the smoke source intelligent identification sub-module adopts an algorithm based on smoke source fingerprint identification, accurately distinguishes different sources of tobacco smoke and non-tobacco smoke by analyzing particle size distribution of particulate matters generated by different smoke sources or a change trend of chemical components along with time, and performs time-space fusion on smoke source data and infrared data to confirm a smoking behavior; and generating a co-processing instruction, and associating the co-processing instruction to the multimedia intervention sub-module, the air purification sub-module and the air quality announcement sub-module. The invention further discloses a method for monitoring and controlling the second-hand smoke in the closed space by using the device.

The invention discloses a smoking behavior recognition method based on multi-source perception and particle size fingerprint analysis, which comprises the following steps: arranging a particulate matter sensor module, continuously collecting a multi-channel particle size spectrum in a target space in a non-optical mode, and generating a high-resolution particle size distribution sequence according to time; an infrared sensor module is arranged to monitor entering and leaving states of personnel in real time; the short-term change of the concentration distribution of the particulate matters with different particle sizes is calculated through a parameterization formula, and the accurate judgment of the smoking event is realized in combination with the personnel state and the PM2.5 speed increase in the same period. According to the invention, the infrared sensing data stream and the particulate matter concentration data stream are deeply analyzed, and the on-site state of personnel and the specific fluctuation mode of cigarette smoke are accurately identified; through cross cooperation verification of multi-dimensional features, interference of water vapor, mosquito-repellent incense and non-artificial smoke sources can be effectively eliminated, and unification of high detection precision and low false alarm rate is achieved. According to the method, imaging equipment is completely abandoned, and the risk of privacy disclosure is avoided from the source.

The invention provides a bimodal-based pedestrian smoking state recognition system and method. The system comprises an image acquisition module, a data preprocessing module, a feature extraction and fusion module and a smoking detection module. According to the method, the high-precision detection and judgment of the smoking behavior are realized by fusing the complementary sensing characteristics of the visible light and the infrared image. According to the method, a double-flow collaborative deep neural network structure is adopted, semantic features of a visible light image and temperature features of an infrared image are extracted respectively, a cross-modal channel attention mechanism is introduced into a feature layer, and self-adaptive fusion and space focusing of multi-modal information are achieved. According to the method, accurate geometric calibration is not needed, and end-to-end smoking detection and identification can be realized only by ensuring collection time synchronization.

The invention discloses a smoking behavior detection method and device, equipment and a storage medium, and relates to the field of image recognition, and the method comprises the steps: obtaining target video frames corresponding to a to-be-detected target event, extracting posture features based on each target video frame, and determining a target posture corresponding to each target video frame based on the posture features; performing time sequence mode matching on the target video frames based on the timestamps and the target postures corresponding to the target video frames to obtain corresponding mode matching results, and determining a target start frame and a target end frame when the mode matching results represent that the target event is a suspected smoking event; and determining a to-be-verified video frame and a corresponding dynamic region of interest based on the target attitude, and performing object detection on the dynamic region of interest by using a target object detection model to obtain a corresponding target detection result, so as to generate a smoking behavior detection result corresponding to the target event based on the target start frame, the target end frame and the target detection result. According to the invention, the misjudgment rate of smoking behaviors is reduced.

The invention discloses a smoking target identification method based on context learning. The method comprises the following steps: S0, performing coarse screening by using skeleton extraction; the method comprises the following steps: S1, inputting an image into a pre-trained ResNet-101 network for feature extraction; s2, constructing a feature pyramid network, and fusing multi-scale features through a top-down path; s3, splicing to form a global feature map; s4, key features are enhanced through an scSE module; s5, candidate areas are generated and screened; s6, performing context alignment on the candidate areas; and S7, performing classification and regression. The false detection rate and the omission rate in a complex scene can be reduced, and high-precision real-time smoking behavior recognition can be realized as far as possible.

The invention relates to the technical field of civil engineering safety monitoring, and particularly discloses a civil engineering site fire risk intelligent assessment system and method. The system comprises an image module, a smoke module and an AI module, the image module comprises a first camera and a second camera which are used for collecting image information of a construction site from different angles, and the smoke module comprises a plurality of smoke sensors which are used for sensing smoke signals in a specific area of the construction site. And the AI module comprises a position identification sub-module, a behavior identification sub-module and a smoke feature analysis sub-module, and is used for performing multi-dimensional fusion processing on the acquired images and smoke signals, and performing intelligent identification and risk assessment on the smoking behavior of the construction personnel based on a set risk scoring algorithm. According to the method, sensing signal triggering, image behavior analysis and multi-source information matching are combined, a set of three-dimensional and real-time smoking recognition and early warning mechanism is constructed, and the intelligent management level of construction site fire safety can be effectively improved.

This application provides a method, system, device, and storage medium for identifying smoking behavior in industrial parks. The method includes: acquiring video stream data from park surveillance; decoding the video stream data to obtain image frames; performing human target detection on the image frames to determine the location of each human body within the image frames; cropping sub-images containing the human bodies from the image frames based on their locations; performing smoking behavior recognition on the sub-images to determine whether the human body in the sub-image is smoking; and outputting the smoking behavior recognition results for each human body in the image frames. This method, through a two-stage processing architecture of first human detection and then region cropping, transforms the problem of identifying small-target smoking behavior in a large-scale park surveillance scenario into a refined behavior discrimination problem at a normalized scale. This effectively alleviates the contradiction between sparse features of small targets and background interference, improving the balance between recognition accuracy and computational efficiency.

The utility model discloses a wrist wearing type smoking cessation instrument, and relates to the technical field of smoking cessation instruments. A display screen, a setting button and an alarm are arranged on the front side wall of the smoking cessation instrument main body, a smoke detection sensor and a thermal infrared imager are arranged at the left side end of the smoking cessation instrument main body, a charging socket is formed in the right side end of the smoking cessation instrument main body, and a control module, a GPS positioning module and an equipment power supply are arranged on the inner side of the smoking cessation instrument main body. The smoke detection sensor can monitor the smoke concentration around the user in real time, and when smoke is detected, the user is reminded through the alarm, so that the user can timely realize the smoking behavior and correct the smoking behavior; and the thermal infrared imager can monitor the change of local temperature at the hand position of the user and assists in judging whether the user is smoking or not, so that the smoking cessation effect is further enhanced. The electrode slice on the rear side wall of the smoking cessation instrument body is matched with the conductive gel slice, the wrist of the user can be slightly electrically stimulated through the electrical stimulation module, and reminding and intervention are given when the user smokes.

The present application belongs to the technical field of smoking monitoring, and particularly relates to an indoor smoking management method based on infrared monitoring. The indoor smoking management method based on infrared monitoring comprises at least the following steps: a monitoring step of monitoring the ambient temperature in a monitoring range in real time; a smoking judgment step of regarding a smoking event as occurring when a high temperature point higher than a preset smoking temperature point is judged to exist; a judgment confirmation step of continuously performing the real-time monitoring step to obtain the motion trail of the high temperature point after the high temperature point occurs, and performing reconfirmation of the smoking behavior; and a notification step of performing alarm and recording the smoking event after the smoking event is judged and confirmed to occur. The indoor smoking management method based on infrared monitoring can accurately judge the occurrence of the smoking behavior through infrared heat detection, image analysis and behavior judgment, and timely perform alarm processing.

The application discloses a smoking behavior detection method and device, equipment and storage medium, and relates to the technical field of image recognition. The method comprises the following steps: acquiring a pedestrian area in a to-be-detected image; performing smoking detection on the pedestrian area by using a fusion model obtained by fusing multiple models; the fusion model comprises a pedestrian posture recognition submodel, a cigarette recognition submodel and a pedestrian gesture and mouth shape recognition submodel; and whether there is a smoking behavior in the to-be-detected image is judged according to the recognition result of the submodel in the fusion model. By using the detection mode of multiple model fusion, comprehensive judgment is performed on the image features of the cigarette, the posture and action of the person and the gesture and mouth shape of smoking. Compared with a single detection model, the accuracy of smoking detection is greatly improved, and the object of smoking detection is the pedestrian area in the to-be-detected image, that is, the cigarette detection is performed only in the area where the pedestrian is located, so that background interference is avoided, and the situation of missing report or false report is avoided.

The application relates to a smoking identification method and a related device, which comprises the following steps: obtaining a target image to be processed, and extracting a candidate region image from the target image based on preset region parameters; performing image correction processing on the candidate region image to obtain a target region image; inputting the target region image into a pre-trained target detection model to obtain at least one candidate detection frame and a confidence corresponding to the candidate detection frame; judging whether a target detection frame meeting a target constraint condition exists in the candidate detection frame, and if the target detection frame exists, determining that a smoking behavior exists in the target image to be processed, and generating an effective region image. The scheme provided by the application can pre-process the image to be detected, and then input the image into a target detection model for detection, so as to judge whether a smoking behavior exists in the image to be detected, improve the identification precision and efficiency of the smoking behavior, and reduce the false detection and missed detection.

This invention belongs to the field of heated cigarette technology, specifically relating to a control method for heated cigarettes. The adaptive temperature control method for the pre-smoking stage includes: determining the user's required smoke volume based on the user's smoking depth and duration during the smoking stage; simultaneously determining the input power of the temperature controller for the smoking stage based on the user's required smoke volume; and adaptively calculating the output power of the temperature controller for the pre-smoking stage, so that the required smoke volume can be rapidly generated according to the user's smoking behavior. The adaptive temperature control method for the smoking stage includes: adjusting the output power of the heating element based on the user's required smoke volume calculated by the data analyzer using differential pressure data. This invention transforms consumer smoking behavior characteristics into a time pointer in the control method, which is beneficial for the targeted design of heating programs such as preheating programs, smoking process heating programs, and smoking interval heating programs, and also provides technical support for personalized customization of heated cigarettes.

This application provides a behavior recognition method and electronic device to address the problem of inaccurate recognition of smoking behavior in existing technologies, which poses a safety hazard. In this application embodiment, after acquiring an image to be recognized, the electronic device determines the target distance between the target in the image and the acquisition device, detects targets related to the target behavior, and adjusts the focal length of the acquisition device according to the target distance. This results in a clearer image of the target, with its enlarged area within the image. Behavior recognition is then performed based on this target image, accurately identifying the presence of the corresponding behavior and reducing safety risks.

The utility model belongs to the field of intelligent monitoring, and provides a multifunctional smoking behavior monitoring device which comprises an installation support, a machine shell with an image acquisition window and a smoke detection window is arranged on the installation support, and an image acquisition system, a smoke acquisition module and a main control module are arranged on the machine shell. The image acquisition system comprises a thermal imaging module and a visible light camera module; through cooperative work of the smoke collection module, the thermal imaging module and the motion capture module, monitoring is performed from multiple dimensions of smoke, temperature and motion, compared with a traditional smoke alarm, the accuracy of smoking behavior monitoring is improved, the false alarm rate is reduced, the face image of a smoker can be captured through the face recognition module, and the smoke alarm is more intelligent. The method can accurately position the identity of the smoker, and mainly monitors the smoker with the smoking history, thereby facilitating the afterward tracing and management, and effectively restraining the illegal smoking behavior.

The invention discloses a smoking hidden danger identification method, device and system, and the method comprises the steps: obtaining multi-source image data, carrying out the preprocessing, obtaining a training data set, inputting the training data set into a deep learning model containing a path aggregation network, generating a multi-level feature map, executing the sparse anchor point screening, completing the model training, and obtaining a smoking hidden danger identification result. Obtaining an initial smoking hidden danger identification model; regularly collecting real-time smoking behavior images as incremental data, fusing the incremental data with the training data set to obtain an iterative data set, and performing lightweight fine tuning on the small target detection head of the initial smoking hidden danger recognition model to obtain an updated smoking hidden danger recognition model; and inputting a real-time image stream of an industrial site into the updated smoking hidden danger recognition model, executing reasoning analysis, and outputting a smoking hidden danger recognition result. According to the method, the small target detection rate is improved through the multi-level feature map, and the accuracy and stability of smoking hidden danger recognition are improved.

This invention discloses an intelligent detection method for smoking behavior in no-smoking areas based on target detection and image segmentation. The method involves scaling up the image / video frames of the no-smoking area to be detected, and then preprocessing the scaled images using a standardized approach. A YOLO11 target detection model is then constructed to detect smoking-related targets in the preprocessed no-smoking area images, generating detection images. Rectangular regions containing suspected cigarettes and related human hands are cropped from the detection images and input into a SAM image segmentation model for optimization. Simultaneously, the center coordinates of the suspected cigarettes are also input into the model, achieving pixel-level precise segmentation of the cigarettes, hands, and smoke, extracting accurate target contour features. Finally, by combining spatial relationships and feature judgment rules, the final identification and determination of smoking behavior is completed.

This invention discloses a method and system for detecting smoking behavior in industrial scene surveillance videos. It employs a combination of metric learning and temperature detection, using metric learning to extract features and calculate distances for target classification. A TransReID neural network is used as the feature extraction network to extract features combining smoking actions and cigarettes. A self-attention mechanism is employed for better feature extraction and accurate identification of smoking behavior, overcoming the shortcomings of traditional smoking detection methods. By establishing a template library, feature maps of the worker image frames to be detected are matched with feature maps in the library, improving the accuracy of smoking action judgment.

1. Name of the product in this design: Smoking cessation stick. 2. Purpose of this design: To replace cigarette substitutes used for smoking. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application provides a smoking environment management method and system, the method comprises the following steps: acquiring temperature parameters, gas concentration parameters and temperature and humidity parameters; preprocessing the environment data to obtain processing data, sequentially filtering isolated high temperature points and drift correction on the temperature parameters, sequentially verifying and moving average filtering on the gas concentration parameters, and cross correction on the temperature and humidity parameters; executing a multi-level verification process on the processing data to screen out suspected smoking events; in response to the screened suspected smoking events, calculating the multi-dimensional feature scores corresponding to the events; weighting and summing the multi-dimensional feature scores and the selected weight matrix to obtain a smoking comprehensive index; determining whether a smoking behavior occurs according to the smoking comprehensive index; if yes, generating and sending a control instruction to an environment adjusting device for linkage control, which significantly improves the timeliness of response and provides the possibility for timely intervention and suppression.

This invention discloses a driver smoking behavior detection method based on an improved YOLOv7, relating to the field of visual positioning technology. The method first constructs and trains a driver smoking behavior model based on an improved YOLOv7; then, it acquires monitoring video from the car's cockpit and extracts image data for each frame; it loads the driver smoking behavior model based on the improved YOLOv7 to detect whether any person appears in each frame; if a person appears, it loads a DeepSORT detection model to detect the person in the current frame and assigns them an ID; based on the person's ID and coordinates, it extracts the person's image from the current frame and inputs it into a pre-trained cigarette butt detection model for cigarette butt detection; when a cigarette butt is detected, it retains the positional and temporal information of the detected cigarette butt in the current frame; it compares the retained temporal information of the frames with pre-matching information to calculate the goodness of fit, and uses the goodness of fit to determine whether smoking behavior exists, thus improving the accuracy and practicality of smoking behavior detection.

This device can be added to various e-smoking products and provides the ability to monitor and capture the usage of individual users and e-smoking products under normal conditions. [Solution] The product use and behavior device 100 includes a housing that defines a central compartment. A control device 102 is positioned within the central compartment. The control device includes a connector 110. The connector is configured to connect the product use and behavior device to an e-smoking article in an operable and detachable manner. The e-smoking article includes a heating element for heating a tobacco product. The e-smoking article includes a battery 118. A sensor circuit 116 is structured to collect at least one use data characteristic of smoking behavior. Smoking behavior is associated with the use of at least one of the heating element and the battery. Local memory is structured to store at least one use data characteristic of smoking behavior.