400 results about "Foreground detection" patented technology

Systems and methods for foreground analysis in real-time video include background subtraction and foreground detection, shadow removal, quick lighting change adaptation, static foreground region detection, foreground fragment reduction, and frame level change detection. Processes include background image extraction and foreground detection, integrating texture information of the background image and a current frame to remove false positive foreground areas resulting from lighting changes, integrating pixel intensity information by determining a cross-correlation of intensities between a current frame and the background image for each pixel in a foreground mask to remove image shadows. Static foreground region detection and fragment reduction are also included.

The invention discloses a static gesture recognition method based on a finger contour and decision-making trees. The method comprises the steps that a Kinect depth image is used as a data source at first, the approximate coordinates of the palm are positioned through the Kinect skeleton tracking function, and a square area containing the palm is cut out with the coordinates as a center; the self-adaptive adjacent value method is used for conducting foreground detection on the area, and the palm contour is detected after appropriate image morphology processing is conducted on the foreground image; a circumference sequence curve is used for conducting modeling on the palm contour, and the extreme point pair method is utilized for accurately distinguishing each finger contour and a wrist contour and building gesture feature sets; at last, the decision-making trees are used for training and recognizing the gesture feature sets with different finger numbers.

The invention discloses a moving target detection method, which comprises the steps of background modeling and foreground detection, wherein the background modeling is performed by using a gaussian mixture model, and an operation of model updating is performed so as to obtain B gaussian distributions, for a pixel in a frame image to be detected, if in the B gaussian distributions subjected to sequencing, at least a gaussian distribution is matched with the current pixel value, the pixel is a background pixel, otherwise, and the pixel is determined as a foreground pixel. According to the invention, foreground noises can be effectively filtered so as to obtain an extremely clean background, thereby, an efficient denoising effect is kept, and the accuracy and completeness of foreground object detection are enhanced.

An object detection method and an object detection system, suitable for detecting moving object information of a video stream having a plurality of images, are provided. The method performs a moving object foreground detection on each of the images, so as to obtain a first foreground detection image comprising a plurality of moving objects. The method also performs a texture object foreground detection on each of the images, so as to obtain a second foreground detection image comprising a plurality of texture objects. The moving objects in the first foreground detection image and the texture objects in the second foreground detection image are selected and filtered, and then the remaining moving objects or texture objects after the filtering are output as real moving object information.

The invention discloses a method of judging a shielding state of a pick-up lens based on a video image signal. The method is characterized by conducing image processing for a video image by taking of a camera, extracting background of the video, obtaining foreground, conducting binarization processing for the foreground, dividing a foreground block, according to the size of a pixel area of the foreground block, obtaining a foreground detection unit, filtering a nuisance area in the foreground detection unit, obtaining a suspicious sheltering area, and if the sheltering area value is judged to be larger than preset sheltering threshold value, the camera is confirmed to be sheltered. The method is used for solving the technical problem that the camera is sheltered so as to cause monitoring failure. The method has the advantages of being simple in method, high in precision, and wide in adaptability.

The invention provides a smoke detection method based on a support vector machine. The method comprises the following steps of: (101) establishing background images: partitioning a continuous frame image into W*H blocks and then establishing background image for each subblock; (102) acquiring a foreground detection area; (103) calculating the characteristics of the foreground detection area; and (104) recognizing smoke areas according to the characteristics by adopting the support vector machine. The invention also provides a smoke detection device based on the support vector machine. The smoke detection method and the device based on the support vector machine can effectively detect smoke which slowly changes so as to solve the practical problem of recognition difficulty of smoke slowly changed in large indoor scenes, such as warehouses, large laboratories, and the like.

The invention discloses a method for directional cross-border detection and mixing line detection in a video. The method includes the following steps of S1, inputting a monitoring video, S2, initializing the video, wherein the mixing line and an interesting region are set in the video, and the minimum filtering movement target area is selected according to the resolution ratio of the input video to be detected, S3, detecting the foreground, wherein movement targets are detected and tracked, and the movement target needing to be detected is filtered out, S4, carrying out mixing line detection or cross-border detection, wherein the cross-border detection comprises the two phenomena of intrusion detection and fleeing detection, S5, carrying out the S3 and the S4 repeatedly for every later new movement target. By means of the method for directional cross-border detection and mixing line detection in the video, the movement targets are filtered, targets which do not need to be detected such as birds and small animals are excluded, detection accuracy is improved, detections of leaving and the movement direction of the movement target can be achieved on the basis of mixing line detection and intrusion detection, detection is fully functional, and the method better meets actual application requirements.

A method for foreground detection using multi-modality fusion graph cut includes the following steps. A video frame of a video sequence is inputted. A foreground region in the video frame is designated using adaptive background Gaussian Mixture Model and a threshold value. The foreground region is segmented using a multi-modality fusion graph cut procedure. A computer program product using the method and a system for foreground detection using multi-modality fusion graph cut are also disclosed herein.

The invention provides an automatic labeling method for a human joint based on a monocular video. The automatic labeling method comprises the following steps: detecting a foreground and storing the foreground as an area of interest; confirming an area of a human body, cutting a part and obtaining an outline of sketch; obtaining a skeleton of the human body and obtaining a key point of the skeleton; utilizing a relative position of face and hands to roughly estimate the gesture of human body; and automatically labeling the point of human joint. During an automatic labeling process, the sketch outline information, skin color information and skeleton information from sketch of human body are comprehensively utilized, so that the accuracy for extracting the joint point is ensured. According to the automatic labeling method provided by the invention, the accurate and efficient cutting for the part of human body is performed, the gesture information of each limb part is obtained and a beneficial condition is supplied to the next operation for obtaining and treating feature vectors of human body.

The invention relates to a self-calibration method for a pickup camera based on an image and motion information of a moving target in a video, which comprises: carrying out prospect detection for the video containing the moving target, and extracting moving target regions; extracting characteristics from each moving target region; roughly classifying the moving target regions; extracting mutually vertical three vanishing points from the images and the motion information of the massive moving target regions; and combining height information of the pickup camera to finish full calibration of the pickup camera for monitoring a scene. The method replaces workload and error of manual calibration, and is used for obtaining an actual point distance of a three-dimensional world through point distance in the images and obtaining an actual line included angle of the three-dimensional world through a line included angle in the images based on measurement of the images or the video, used for monitoring object classification and recognition in the scene and compensating inherent perspective deformation of two-dimensional image characteristics, and used for monitoring object recognition in the scene based on a three-dimensional model to obtain a three-dimensional posture and a track and effectively help a system to comprehend behaviors in the scene.

An image surveillance system and a method of detecting whether an object is left behind or taken away are provided. The image surveillance system includes: a foreground detecting unit which detects a foreground region based on a pixel information difference between a background image and a current input image; a still region detecting unit which detects a candidate still region by clustering foreground pixels of the foreground region, and determines whether the candidate still region is a falsely detected still region or a true still region; and an object detecting unit which determines whether an object is left behind or taken away, based on edge information about the true still region.

The invention provides a real-time high-precision people stream counting method, which comprises two steps of moving target foreground extraction and foreground area pedestrian detection, wherein the step of moving target foreground extraction comprises the following steps: carrying out foreground detection on an obtained video frame sequence to obtain a foreground area comprising moving targets, such as pedestrians, vehicles and the like; and the step of foreground area pedestrian detection comprises the following steps: carrying out pedestrian detection on the foreground area by utilizing an off-line training deformable component model to determine the amount and the positions of the pedestrians in the foreground area, and tracking the subsequent movement of pedestrian targets by taking a current frame detection result as start to judge and record a situation that people streams enter and leave a gate. The method extracts the foreground area which comprises a target on the basis of a background subtraction method, so that an algorithm satisfies a real-time calculation condition, the pedestrian detection based on the deformable component model guarantees the high precision of people stream counting, a real-time people stream counting method with high precision and good shielding resistance is provided, and the real-time high-precision people stream counting method exhibits high practical value and good development prospect.

The invention discloses a method for detecting and tracking a night running vehicle. The method is implemented according to the following steps of: (1) foreground detection: selecting vehicle lamp brightness to carry out foreground detection, and detecting a vehicle lamp region through detecting whether the brightness of each frame of pixel point in a video stream is larger than a set threshold or not; (2) noise elimination: removing most noise points from a binary image obtained from the step (1) and more accurately obtaining a foreground target; (3) vehicle lamp matching: pairing two vehicle lamps according to a corresponding principle, finding out a big front lamp pair and representing the vehicle by using the big front lamp pair; (4) vehicle lamp pair tracking: after finishing the vehicle lamp pairing according to the steps, tracking the vehicle lamp so as to realize the tracking of the vehicle; and (5) after retracking the target, matching the vehicle lamp pairs and finally obtaining a vehicle to be detected. The method for detecting and tracking the night running vehicle, disclosed by the invention, has the following advantages that: the night vehicle is detected by using the vehicle lamp characteristic; and in the method, simplicity for extracting algorithm characteristic and stable vehicle detecting effect are obtained.