Abstract: A queue information analyzing method is applied to an image analyzing apparatus. A monitoring image captured by the image analyzing apparatus has a triggering area. The queue information analyzing method includes identifying a first candidate object stayed within the triggering area, forming a sampling range via the first candidate object, determining whether a second candidate object stayed within the sampling range belongs to a queue of the first candidate object, and acquiring an amount and a accumulated time of candidate objects about the queue.

Abstract: A queue analyzing method is applied to an image monitoring apparatus and can automatically generate an interval threshold according to position variation of objects. The queue analyzing method includes computing a plurality of intervals between all objects inside an image, dividing the plurality of intervals at least into a first group corresponding to a low interval range and a second group corresponding to a high interval range, computing an interval mean value and an interval amending value of objects inside the first group, utilizing the interval mean value and the interval amending value to generate the interval threshold, and marking some adjacent objects conforming to the interval threshold within the image.

Abstract: An image calibrating method is applied to a first monitoring image and a second monitoring image partly overlapped with each other. The image calibrating method includes detecting a plurality of first marking points and second marking points about a target object on the first monitoring image and the second monitoring image, computing a first trace and a second trace formed by the first marking points and the second marking points, setting a plurality of first estimating points and second estimating points on stretching sections on the first trace and the second respectively within the second monitoring image and the first monitoring image, and utilizing the first marking points and the second estimating points and/or the first estimating points and the second marking points to compute a shift between the first monitoring image and the second monitoring image.

Abstract: An object identification method determines whether a first monitoring image and a second monitoring image captured by a monitoring camera apparatus have the same object. The object identification method includes acquiring the first monitoring image at a first point of time to analyze a first object inside a first angle of view of the first monitoring image, acquiring the second monitoring image at a second point of the time different from the first point of time to analyze a second object inside the first angle of view of the second monitoring image, estimating a first similarity between the first object inside the first angle of view of the first monitoring image and the second object inside the first angle of view of the second monitoring image; and determining whether the first object and the second object are the same according to comparison result of the first similarity with a threshold.

Abstract: An image stitching method applied to a monitoring camera apparatus with a first image receiver and a second image receiver for acquiring a first image and a second image. The image stitching method includes detecting a plurality of first features in the first image and a plurality of second features in the second image, dividing the plurality of first features at least into a first group and a second group and further dividing the plurality of second features at least into a third group, analyzing the plurality of first features and the plurality of second features via an identification condition to determine whether one of the first group and the second group is matched with the third group, and utilizing two matched groups to stitching the first image and the second image.

Abstract: A queue analyzing method is applied to an image monitoring apparatus and can automatically generate an interval threshold according to position variation of objects. The queue analyzing method includes computing a plurality of intervals between all objects inside an image, dividing the plurality of intervals at least into a first group corresponding to a low interval range and a second group corresponding to a high interval range, computing an interval mean value and an interval amending value of objects inside the first group, utilizing the interval mean value and the interval amending value to generate the interval threshold, and marking some adjacent objects conforming to the interval threshold within the image.

Abstract: A queue analyzing method is applied to an image monitoring apparatus for determining whether a rear object belongs to a queue of a front object. The queue analyzing method includes computing an angle difference and an interval between the rear object and the front object, transforming an original interval threshold into an amended interval threshold via the angle difference, comparing the interval with the amended interval threshold, and determining the rear object and the front object belong to the same queue when the interval is smaller than the amended interval threshold.

Abstract: A queue analyzing method is applied to an image monitoring apparatus for determining whether a rear object belongs to a queue of a front object. The queue analyzing method includes computing an angle difference and an interval between the rear object and the front object, transforming an original interval threshold into an amended interval threshold via the angle difference, comparing the interval with the amended interval threshold, and determining the rear object and the front object belong to the same queue when the interval is smaller than the amended interval threshold.

Abstract: An image stitching method applied to a monitoring camera apparatus with a first image receiver and a second image receiver for acquiring a first image and a second image. The image stitching method includes detecting a plurality of first features in the first image and a plurality of second features in the second image, dividing the plurality of first features at least into a first group and a second group and further dividing the plurality of second features at least into a third group, analyzing the plurality of first features and the plurality of second features via an identification condition to determine whether one of the first group and the second group is matched with the third group, and utilizing two matched groups to stitching the first image and the second image.

Abstract: An image calibrating method is applied to a first monitoring image and a second monitoring image partly overlapped with each other. The image calibrating method includes detecting a plurality of first marking points and second marking points about a target object on the first monitoring image and the second monitoring image, computing a first trace and a second trace formed by the first marking points and the second marking points, setting a plurality of first estimating points and second estimating points on stretching sections on the first trace and the second respectively within the second monitoring image and the first monitoring image, and utilizing the first marking points and the second estimating points and/or the first estimating points and the second marking points to compute a shift between the first monitoring image and the second monitoring image.

Abstract: An image identifying method is applied to a monitoring camera and a monitoring camera system and used to determine whether a target object is a leaving object or a missing object. The image identifying method includes acquiring a foreground region within a monitoring image corresponding to the target object, analyzing whether the target object inside the foreground region conforms to a variant feature, and comparing the foreground region with a reference image for determining the target object belongs to the leaving object or the missing object when the foreground region does not conform to the variant feature.

Abstract: An object counting device includes an image capturer and a processor. The image capturer captures an image sequence related to a gate within a field of view. When an object in the image sequence passes through an entry/exit reference corresponding to the gate, the processor determines a moving direction of the object. When the processor determines that the object moves from a first side of the gate towards a second side of the gate, the processor counts the object into a first accumulated number. When the object exits from the field of view, the processor records an exit position of the object. The processor determines whether to count the object into a second accumulated number according to the exit position.

Abstract: A convolutional neutral network identification efficiency increasing method is applied to a related device. The convolutional neutral network identification efficiency increasing method includes analyzing an input image to acquire foreground information, utilizing the foreground information to generate a foreground mask, and transforming the input image into an output image via the foreground mask. The output image is used to be an input of the convolutional neutral network identification for preferred object identification efficiency.

Abstract: A pedestrian detection method is applied to a monitoring camera. The pedestrian detection method includes forming a first detecting window on at least one monitoring frame via an object analysis function, utilizing a human form detection function to modulate the first detecting window for forming a second detecting window, analyzing the second detecting window via a human local detection function to mark an upper detecting window about a pedestrian on the monitoring frame, and determining whether to calibrate the second detecting window via analysis of the upper detecting window.

Abstract: A machine continuous learning method with a neural network object classifying function is applied to a monitoring camera apparatus having a processor with an object classifier. The machine continuous learning method includes utilizing the processor to receive an image, utilizing the object classifier to analyze the image for generating a first parameter and a second parameter, utilizing the processor to determine whether the first parameter belongs to at least one cluster established by human feedback, and utilizing the processor to output a label of the at least one cluster or the second parameter generated by the object classifier according to a determination result.

Abstract: An information measuring method is used for measuring installation information of a camera disposed over a plane with an installation height and oriented towards the plane with a shooting angle. The information measuring method includes steps of disposing a first reference point and a second reference point on the plane; measuring a first distance and a second distance between the first reference point, the second reference point and a third reference point; capturing an image including the first reference point, the second reference point and the third reference point; analyzing the image to define a first reference line and a second reference line; determining a first angle and a second angle according to the first reference line, the second reference line and a first normal line; and determining the installation height and/or the shooting angle according to the first distance, the second distance, the first angle and the second angle.

Abstract: A motion detection method is applied to a monitoring camera apparatus with motion detection function. The motion detection method includes analyzing a pixel value of each frame from a video stream changed over time, defining a first period and a second period having the pixel value greater than a triggering threshold respectively as a first event and a second event, comparing a time length of the first period with a filtering threshold to acquire time difference between an end point of the first period and a beginning point of the second period, comparing the time difference with a merging threshold, and acquiring relation between the first event and the second event according to comparison results of the filtering threshold and the merging threshold, so as to determine a detecting period of the motion detection function for actuation.

Abstract: A convolutional neutral network identification efficiency increasing method is applied to a related device. The convolutional neutral network identification efficiency increasing method includes analyzing an input image to acquire foreground information, utilizing the foreground information to generate a foreground mask, and transforming the input image into an output image via the foreground mask. The output image is used to be an input of the convolutional neutral network identification for preferred object identification efficiency.

Abstract: A pedestrian detection method is applied to a monitoring camera. The pedestrian detection method includes forming a first detecting window on at least one monitoring frame via an object analysis function, utilizing a human form detection function to modulate the first detecting window for forming a second detecting window, analyzing the second detecting window via a human local detection function to mark an upper detecting window about a pedestrian on the monitoring frame, and determining whether to calibrate the second detecting window via analysis of the upper detecting window.

Abstract: An image processing method includes steps of receiving an image sequence; when at least one object appears in the image sequence, analyzing a moving trajectory of each object; extracting at least one characteristic point from each moving trajectory; classifying the at least one characteristic point of each moving trajectory within a predetermined time period into at least one cluster; and storing at least one characteristic parameter of each cluster.