Vivotek Inc.

Cheng-Chieh Liu

Chih-Yen Lin

Wei-Ming Liu

Fu-Min Wang

Chao-Ming Chang

Szu-Mo Chang

Kuan-Yu Lin

Chao-Tan Huang

Hsun-Shun Yu

Chia-Wei Chi

Wei-Ting Wu

Sheng-Yuan Chen

Shih-Ming Huang

Shaw-Pin Chen

Hui-Chih Wang

Chia-Po Wei

Shun-Hsun Yu

Chang-Li Chen

I-Lun Chen

Wen-Pin Hsieh

Ting-Wei Hsu

An image identification method is applied to an image surveillance apparatus and includes utilizing an object detecting function to generate a first bounding box and a second bounding box within a surveillance image, utilizing a foreground detecting function to generate a plurality of markers within the surveillance image, defining some of the plurality of markers which conform to the first bounding box as a first marker group and further defining other markers which do not conform to the first bounding box as a second marker group, determining whether the second marker group conforms to the second bounding box, and deciding whether the second bounding box belongs to an error of the object detecting function in accordance with a determination result.

Image identification method and image surveillance apparatus

An object identification method is applied to a surveillance system. The surveillance system includes at least one surveillance apparatus. The object identification method includes acquiring a plurality of first feature vectors of a first moving object and a plurality of second feature vectors of at least one second moving object within a series of surveillance images from the surveillance apparatus, transforming the first feature vectors and the second feature vectors respectively into a first cluster distribution set and at least one second cluster distribution set, comparing similarity of the first cluster distribution set and the at least one second cluster distribution set, and setting a similarity ranking of the first cluster distribution set and the at least one second cluster distribution set according to a comparison result so as to determine whether the first moving object and the at least one second moving object are the same.

Object identification method and related surveillance system

A surveillance region identifying method is used to analyze a region feature of a surveillance region covered by a surveillance apparatus. The surveillance region identifying method includes analyzing all track information within a series of images acquired by the surveillance apparatus to acquire an appearing point and a disappearing point of each track information, utilizing cluster analysis to define a main appearing point cluster of the appearing points, computing enter vectors of all appearing points inside the main appearing point cluster, and analyzing vector angles of all enter vectors of the main appearing point cluster to define an entrance of the surveillance region.

Surveillance region identifying method and surveillance apparatus

An object counting method is applied to a surveillance camera and used to determine an amount and a direction an object passing over a surveillance region. The object counting method includes acquiring a plurality of known traces, dividing the plurality of known traces into a first group passing the surveillance region along a first direction and a second group passing the surveillance region along a second direction, computing a first start point computed value of the first group and a second start point computed value of the second group to acquire a start point connection vector, and comparing the start point connection vector with a trace vector of a target object to determine whether the target object passes over the surveillance region along the first direction or the second direction.

Object counting method and surveillance camera

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.

Object identification method and related monitoring camera apparatus

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.

Information measuring method and information measuring system

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.

Image identifying method and related monitoring camera and monitoring camera system

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.

Queue information analyzing method and related image analyzing apparatus

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.

Queue analyzing method and image monitoring apparatus

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.

Image calibrating method and related monitoring camera system

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.

Image stitching method and related monitoring camera apparatus

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.

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.

Object counting device, object counting system and object counting method

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.

Convolutional neutral network identification efficiency increasing method and related convolutional neutral network identification efficiency increasing device

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.

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Taipei, Taiwan

Cheng-Chieh Liu