1577 results about "Pedestrian detection" patented technology

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 classifier for determining whether an instance belongs to a particular class of instances of a plurality of classes, the classifier comprising: a plurality of first classifiers that operate on an instance to provide an indication as to which class the instance belongs, each of which classifiers is trained on a different subset of training instances from a same set of training instances wherein each training subset comprises a group of training instances that share at least one characteristic trait and different subsets have a different at least one characteristic trait; and a second classifier that operates on the indications provided by the first classifiers to provide an indication as to which class the instance belongs.

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.

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.

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(x_t|Z^t−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(z_t|x_t) 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(x_t|Z^t) from the observation likelihood distribution p(z_t|x_t) and the prior probability distribution p(x_t|Z^t−1), and using marginal space learning to estimate the prior probability distribution p(x_t+1|Z^t) for a next image frame given the posterior probability distribution p(x_t|Z^t) and a probability p(x_t+1|x_t), 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.

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 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 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.