1582 results about "Pedestrian detection" patented technology

A pedestrian warning or alert system and method are disclosed. The warning system is mounted on an electric vehicle. The warning system includes a front speaker system, a rear speaker system, a front pedestrian detector, a rear pedestrian detector, and an electronic control unit (ECU) electronically coupled to the front speaker system, the rear speaker system, the front pedestrian detector, and the rear pedestrian detector. The ECU commands the front speaker system to emit a warning sound based on the front pedestrian detector detecting a pedestrian-shaped object and commands the rear speaker system to emit a warning sound based on the rear pedestrian detector detecting a pedestrian-shaped object. The ECU is coupled to a microphone, the signal of which is used to estimate an ambient noise level. The intensity and frequency of the warning sound commanded from the front and / or rear speaker system is based on the ambient noise level.

An apparatus, method for detecting critical areas and a pedestrian detection apparatus using the same are provided. An application of the pedestrian detection system is provided to help limit critical urban environment to particular areas. Contrary to traditional pedestrian detection systems that localize every pedestrians appearing in front of the subject vehicle, the apparatus first finds critical areas from urban environment and performs a focused search of pedestrians. The environment is reconstructed using a standard laser scanner but the subsequent checking for the presence of pedestrians is performed by incorporating a vision system. The apparatus identifies pedestrians within substantially limited image areas and results in boosts of timing performance, since no evaluation of critical degrees is necessary until an actual pedestrian is informed to the driver or onboard computer.

A computer implemented method for detecting the presence of one or more pedestrians in the vicinity of the vehicle is disclosed. Imagery of a scene is received from at least one image capturing device. A depth map is derived from the imagery. A plurality of pedestrian candidate regions of interest (ROIs) is detected from the depth map by matching each of the plurality of ROIs with a 3D human shape model. At least a portion of the candidate ROIs is classified by employing a cascade of classifiers tuned for a plurality of depth bands and trained on a filtered representation of data within the portion of candidate ROIs to determine whether at least one pedestrian is proximal to the vehicle.

A computer implemented method for detecting the presence of one or more pedestrians in the vicinity of the vehicle is disclosed. Imagery of a scene is received from at least one image capturing device. A depth map is derived from the imagery. A plurality of pedestrian candidate regions of interest (ROIs) is detected from the depth map by matching each of the plurality of ROIs with a 3D human shape model. At least a portion of the candidate ROIs is classified by employing a cascade of classifiers tuned for a plurality of depth bands and trained on a filtered representation of data within the portion of candidate ROIs to determine whether at least one pedestrian is proximal to the vehicle.

An apparatus, method for detecting critical areas and a pedestrian detection apparatus using the same are provided. An application of the pedestrian detection system is provided to help limit critical urban environment to particular areas. Contrary to traditional pedestrian detection systems that localize every pedestrians appearing in front of the subject vehicle, the apparatus first finds critical areas from urban environment and performs a focused search of pedestrians. The environment is reconstructed using a standard laser scanner but the subsequent checking for the presence of pedestrians is performed by incorporating a vision system. The apparatus identifies pedestrians within substantially limited image areas and results in boosts of timing performance, since no evaluation of critical degrees is necessary until an actual pedestrian is informed to the driver or onboard computer.

The invention discloses a cross-camera pedestrian detection tracking method based on depth learning, which comprises the steps of: by training a pedestrian detection network, carrying out pedestrian detection on an input monitoring video sequence; initializing tracking targets by a target box obtained by pedestrian detection, extracting shallow layer features and deep layer features of a region corresponding to a candidate box in the pedestrian detection network, and implementing tracking; when the targets disappear, carrying out pedestrian re-identification which comprises the process of: after target disappearance information is obtained, finding images with the highest matching degrees with the disappearing targets from candidate images obtained by the pedestrian detection network and continuously tracking; and when tracking is ended, outputting motion tracks of the pedestrian targets under multiple cameras. The features extracted by the method can overcome influence of illuminationvariations and viewing angle variations; moreover, for both the tracking and pedestrian re-identification parts, the features are extracted from the pedestrian detection network; pedestrian detection, multi-target tracking and pedestrian re-identification are organically fused; and accurate cross-camera pedestrian detection and tracking in a large-range scene are implemented.

The invention discloses an improved weighting region matching high-altitude video pedestrian recognizing method (KS-WRM (KS-weighting matching region)). The improvement is reflected as follows: 1) confirming a candidate region by utilizing Kalman filtering; 2) fixing a camera or not by adopting different pedestrian detecting strategies; 3) proposing a pedestrian detecting method of an HLS (hue, lightness and saturation) model with multi-characteristic fusion; and 4) dividing pedestrians based on CA obvious region detection of context sensing. According to the invention, pedestrians can be recognized accurately under complex occasions of blur details, complex backgrounds, noise influences and the like. And the improved weighting region matching high-altitude video pedestrian recognizing method (KS-WRM) provided by the invention can be used for pedestrian detection and recognition at places with high visitor flow rate, such as remote sensing satellite images, malls, subway stations, railway stations, airports, as well as intelligent traffic control, intelligent vehicle auxiliary driving, pedestrian flow rate statistics and analysis places.

A vehicle vision system that identifies pedestrians located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate disparity images. A depth map can be produced from the disparity images. Either the depth map or the disparity images are processed and compared to pedestrian templates. A pedestrian list is produced from the results of that comparison. The pedestrian list is processed to eliminate false pedestrians as determined by inverse eccentricity. The pedestrians are then tracked, and if a collision is possible, that information is provided to the driver and / or to an automated collision avoidance or damage or injury mitigation system.

The invention discloses a pedestrian tracking method based on HOG-LBP, comprising the following steps of, A1, sample establishment; A2, feature extraction; A3, SVM model establishment; A4, classifier training; A5, video capture and pretreatment; A6, video pedestrian examination; A7, video pedestrian tracking: applying a particle filtering tracking method based on an HOG-LBP feature to track the pedestrian examined in step A6. The method firstly learns an image pedestrian mode through a support vector machine, and then classifies a moving area in a video sequence and inputs the result to a particle filtering machine to update the particle status, and finally realizes continuous tracking to pedestrian movement in the scene. Because the method collects pedestrian feature by adopting HOG-LBP and uses the particle filtering to track movement, it has good adaptability and stability to movement interleave and sheltering phenomenon in a scene and non-linear feature presented by the movement.

The invention belongs to the field vehicle active safety, and particularly discloses a detection method and system, based on laser radar and binocular camera, for a pedestrian in front of vehicle. The method comprises the following steps: collection data of the front of the vehicle through the laser radar and the binocular camera; respectively processing the data collected by the laser radar and the binocular camera, so as to obtain the distance, azimuth angle and speed value of the pedestrian relative to the vehicle; correcting the information of the pedestrian through a Kalman filter. The method comprehensively utilizes a stereoscopic vision technology and a remote sensing technology, integrates laser radar and binocular camera information, is high in measurement accuracy and pedestrian detection accuracy, and can effectively reduce the occurrence rate of traffic accidents.

A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(xt|Zt−1) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(zt|xt) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(xt|Zt) from the observation likelihood distribution p(zt|xt) and the prior probability distribution p(xt|Zt−1), and using marginal space learning to estimate the prior probability distribution p(xt+1|Zt) for a next image frame given the posterior probability distribution p(xt|Zt) and a probability p(xt+1|xt), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detection hypotheses for the video sequence.

The invention discloses a pedestrian detection method based on an end-to-end convolutional neural network in order to solve the problem that the existing pedestrian detection algorithm has the disadvantages of low detection precision, complex algorithm and difficult multi-module fusion. A novel end-to-end convolutional neural network is adopted, a training sample set with marks is constructed, and end-to-end training is performed to get a convolutional neural network model capable of predicting a pedestrian candidate box and the confidence of the corresponding box. During test, a test picture is input into a trained model, and a corresponding pedestrian detection box and the confidence thereof can be obtained. Finally, non-maximum suppression and threshold screening are performed to get an optimal pedestrian area. The invention has two advantages compared with previous inventions. First, through end-to-end training and testing, the whole model is very easy to train and test. Second, pedestrian scale and proportion problems are solved by constructing a candidate box regression network, the pyramid technology adopted in previous inventions is not needed, and a lot of computing resources are saved.

The invention relates to a pedestrian recognition and tracking method based on an accelerated area Convolutional Neural Network. Firstly, training and testing data set are preprocessed according to the requirements through a robot with an infrared camera to acquire a training dataset and a testing dataset at night, and then, actual target position labeling is conducted on all training and testing photos and is recorded to a sample file; then, the accelerated area Convolutional Neural Network is constructed, the accelerated area Convolutional Neural Network is trained by using the training dataset, and the final probability belonging to a pedestrian area and a bounding box of the area are calculated out from network output by the usage of a non-maximum suppression algorithm; the accuracy of the network is tested by the usage of the testing dataset, and a network model consistent with the requirements is obtained; photos collected by the robot at night are input to an accelerated area Convolutional Neural Network model, and the probability belonging to the pedestrian area and the bounding box of the area are online output by a model in real time. According to the pedestrian detection and tracking method based on the accelerated area Convolutional Neural Network, a pedestrian in an infrared image can be effectively recognized, and real-time tracking for a pedestrian target in an infrared video can be achieved.

The invention discloses a pedestrian detection method based on machine vision and a pedestrian anti-collision warning system based on machine vision. The method includes: acquiring an image of a place in front of an automobile, pre-positioning pedestrians in the processed image, judging a pre-positioned pedestrian area to position the pedestrian area accurately, measuring the distance between the pedestrians and the automobile and judging whether the pedestrians are in a dangerous area or not, and warning the pedestrians in the collision dangerous area. The anti-collision warning system comprises an image acquisition unit, a pedestrian positioning unit, a pedestrian distance measuring unit, and a collision possibility analyzing unit. By using a pedestrian classifier to detect the pedestrians on roads, individual characteristics of the pedestrians are blurred, influence of individual differences and illumination to detecting results is reduced, detection efficiency of the pedestrians is improved. The anti-collision warning system is further used to judge possibility of accidents so as to give warning signals to a driver, so that safety of automotive vehicle driving on the roads is improved.

The invention discloses a pedestrian detection method which comprises the following steps of: obtaining a candidate detection window in the current-frame image by a human body detection method based on HOG characteristics; then determining the corresponding relationship between the current candidate detection window and the human body detection window in the previous-frame image, and standardizing the size of the current candidate detection window; determining to adopt an image division method based on shape prior or an image division method based on the combination of motion information and shape prior for the current standardized candidate detection window according to whether correspondence is established so as to obtain an object division mask of the current standardized candidate detection window; and finally, verifying whether the current candidate detection window is detected wrongly by a pedestrian classifier jointly trained by the object division mask and HOG detection score. Since the motion information and shape prior are integrated into the image division method, the accuracy of object division is improved, and the detection judgment of the candidate detection window is further improved, thus the error detection rate is effectively reduced, and the detection accuracy is improved.

The invention discloses a pedestrian detection method based on a deep learning technology. The method comprises the steps that firstly, a binary classification model is trained through a step-by-step migration strategy on the basis of transfer learning to initialize final model parameters; secondly, pedestrian detection work is completed by adopting and modifying a currently popular and efficient Faster RCNN frame, and on the basis of the CNN characteristics of the frame, not only can images with any scales be processed, but also the detection speed is high. Compared with the prior art, the method has the advantages that the network does not need to be specially designed, existing available data is fully utilized, a good experiment result still can be achieved by adopting a general network structure, the advantages of a deep convolution network are fully achieved, and the advantages of being simple in design, good in robustness, high in detection accuracy and low in omission ratio are achieved.

Provided is a pedestrian detection method. The pedestrian detection method comprises the following steps that a pedestrian positive sample set and a pedestrian negative sample set needed for training a convolutional neural network are prepared; the sample sets are preprocessed and normalized to conform to a unified standard, and a data file is generated; the structure of the convolutional neural network is designed, training is carried out, and a weight connection matrix is obtained during convergence of the network; self-adaptive background modeling is carried out on videos, information of moving objects in each frame is obtained, coarse selection is carried out on detected moving object regions at first, the regions with height to width ratios unsatisfying requirements are excluded, and candidate regions are generated; each candidate region is input into the convolutional neural network, and whether pedestrians exist is judged.

A system and method for detecting and tracking humans, such as pedestrians, in low visibility conditions or otherwise. A night vision camera periodically captures a an infrared image of a road from a single perspective. A pedestrian detection module determines a position of a pedestrian in the frame by processing the captured image. The pedestrian detection module includes a support vector machine to compare information derived from the night vision camera to a training database. A pedestrian tracking module estimates pedestrian movement of the detected pedestrian from in subsequent frames by applying filters. The tracking module uses Kalman filtering to estimate pedestrian movement at periodic times and mean-shifting to adjust the estimation. An output display module interleaves detection frames and tracking frames in generating output video for the display.

The invention relates to a driving auxiliary device and a driving auxiliary method. The driving auxiliary device comprises image acquisition devices, a display, an image processing device and an alarm. Information of images around a vehicle is acquired, analyzed and processed by using a plurality of the image acquisition devices which are arranged in an optimized manner and the image processing device. On the one hand, traditional physical imaging rearview mirrors of an automobile are removed, visual blind areas of a driver are eliminated, and air resistance of the automobile is reduced; on the other hand, the acquired images are processed by the image processing device, information of risks under different driving conditions is identified, to fulfill the auxiliary functions of active safety driving, such as lane change and early warning for side impact in turning, early warning for safe interval, early warning for recognition of road signs, early warning for lane departure, early warning for pedestrian detection and collision avoidance.

The invention relates to a pedestrian detection method based on foreground analysis and pattern recognition, relating to the technical field of image processing. The method comprises the following steps: adopting a Gaussian mixed model to carry out background modeling on a video image and using threshold operation and morphology after-treatment to extract the foreground of the video image; using a contour characteristic and a pedestrian height prior model to analyze the foreground and obtain a preliminary pedestrian detection result; sampling nearby the position of the preliminary pedestrian detection result, using a pedestrian pattern recognition classifier to further judge a sampling area and removing an inaccurate preliminary pedestrian detection result so as to obtain a final pedestrian detection result. The pedestrian detection method not only can improve the degree of accuracy of pedestrian detection, but also can increase the processing speed of pedestrian detection in a video and can be applied to dynamically variable complex occasions.

The invention belongs to the technical field of vehicle-mounted systems and automobile electronics, and relates to a pedestrian and vehicle detecting method and system based on multi-vehicle cooperation. The method comprises the steps that a vehicle-mounted sensor collects the state data of traveling vehicles, and a vehicle-mounted camera is guided to collect images; time stamps, corresponding traveling state data and current GPS data are added to the collected images, and a vehicle-mounted self-organization network is used for broadcasting the images to vehicles on the adjacent network nodes; an image processing module processes local data and the received data to synthesize a panoramic image and carries out pedestrian detection to generate an early-warning prompt instruction; an early-warning prompt module prompts drivers to avoid dangers which possibly appear in a sound and displayer display mode after receiving the instruction of the image processing module. According to the pedestrian and vehicle detecting method and system, the multi-vehicle cooperation way is provided for the first time, view blind zones existing during driving are eliminated for users by means of the different position advantages of different vehicles, and driving safety is improved.

The invention relates to an identity identification method and apparatus with the combination of the gait and the face based on deep learning. The apparatus comprises a video acquisition and preprocessing module, a gait feature extraction module, a face feature extraction module and an identification module. The method includes: acquiring a video stream, and performing pedestrian detection and tracking and face detection on the video stream; performing gait feature extraction on human body images, and calculating quality evaluation scores of gait features; performing face feature extraction onface images, calculating quality evaluation scores of face features, and regarding the face image with the highest quality evaluation score as a to-be-identified face image; and performing weightingon the gait features and the face features according to respective quality scores, and inputting the weighted scores into a SVM classifier for identity identification. According to the method and theapparatus, the quality evaluation scores of the gait features and the face features are calculated, weighting is performed according to the quality scores, advantages of face identification and gait identification are combined, the two identification technologies are complemented, the robustness of an identification system is increased, and the accuracy of figure identity identification is improved.

The invention discloses a multi-target pedestrian detecting and tracking method in monitoring video, comprising the steps that a target detection network based on deep learning is adopted for detecting a first frame of pedestrian image, and an initial rectangular area having one or a plurality of corresponding pedestrian targets can be obtained; based on the initial target area information, the Histogram of oriented gradients feature of a target can be extracted, and kernel function autocorrelation calculating of Fourier expansion domain can be conducted, and the tracking model is initializedbased on the calculating result; based on the target area information of the tracking model, a multi-dimensional construction of a pyramid will be carried out from the second frame of pedestrian image, and the extracting of the Histogram of oriented gradients feature matrix and the kernel function autocorrelation calculating of Fourier expansion domain can be conducted on each scale of the pedestrian rectangular area; the returned check condition is determined, and the identity re-verification and the updating of the tracking model can be conducted on the pedestrian target having returned check. The invention is advantageous in that the problem of drifting models can be resolved; a more accurate pedestrian moving track can be obtained; real-time performance is good.

The invention discloses a deep-learning-based multi-target pedestrian detection and tracking method. The method comprises the following steps: step one, carrying out multi-target pedestrian detectionand joint point extraction on an inputted video and storing obtained position information and joint point information as inputs of a next stage; step two, selecting a key frame at an interval with thecertain number of frames and carrying out apparent characteristic extraction in a pedestrian in the key frame; to be specific, according to the obtained position information and joint point information, extracting upper body part attitude characteristics and color histogram characteristics respectively for pedestrian association between key frames; and step three, carrying out continuous trackingon the pedestrians in the key frames with a threshold slow starting strategy, a block matching rate model detection algorithm, a historical state keeping voting algorithm and a shielding detection method for tracking effect improvement, returning to the step one after tracking ending, and detecting the key frames again to ensure stability of the method.

The invention discloses a pedestrian detection method based on video monitoring, which comprises the following steps of: rapidly detecting a pedestrian by utilizing an expanded gradient histogram characteristic and an Adaboost algorithm, and then further identifying and verifying the detected pedestrian by utilizing the gradient histogram characteristic and a support vector machine. In the invention, experiments based on a standard pedestrian database indicate that the method not only can greatly reduce the detection time, but also markedly improves the detection rate. As one of key technologies of target detection, pedestrian detection has broad application prospect in the fields of video monitoring safety, intelligent traffic management and the like.

The invention provides a pedestrian detection method and system. According to the pedestrian detection method, head and shoulder detection, face detection and whole body detection are carried out on a collected image to obtain one or more kinds of features of the head and shoulders in front, the head and the shoulders at back or the head and the shoulders on the side, feature information of the whole body in front, feature information of the whole body at back, feature information of the whole body on the left and feature information of the whole body on the right. Accordingly, the features of the head and the shoulders in front, at back and on the side and features of the whole body at multiple angles of a target pedestrian with the covered face or a camouflage target pedestrian can be obtained, real-time handling of illegal activities and postmortem analysis are facilitated, and later workload of related staff can be greatly reduced. Defects of a staff gate system based on face detection in the prior art are overcome. The method and system are suitable for face detection in front, can be used for pedestrian detection at back and on the side, all information of a person is basically included in a face snapshot and a whole body snapshot of the person, and later deep analysis is facilitated.

The invention discloses a real-time robust far infrared vehicle-mounted pedestrian detection method. The method comprises the steps of catching a potential pedestrian pre-selection area in an input image through a pixel gradient vertical projection, searching an interest area in the pedestrian pre-selection area through a local threshold method and morphological post-processing techniques, extracting a multi-stage entropy weighing gradient direction histogram for feature description of the interest area, inputting the histogram to a support vector machine pedestrian classifier for online judgment of the interest area, achieving pedestrian detection through multi-frame verification and screening of judgment results of the pedestrian classifier, dividing training sample space according to sample height distribution, building a classification frame of a three-branch structure, and collecting difficult samples and a training pedestrian classifier in an iteration mode with combination of a bootstrap method and an advanced termination method. According to the real-time robust far infrared vehicle-mounted pedestrian detection method, not only is accuracy of pedestrian detection improved, but also a false alarm rate is reduced, input image processing speed and generalization capacity of the classifier are improved, and provided is an effective night vehicle-mounted pedestrian-assisted early warning method.