Hangzhou Hikvision Digital Technology Co., Ltd.

Shiliang Pu

Hai Yu

Linjie Shen

Meng Fan

Hui Mao

Di Xie

Jie Chen

Qiqian Lin

Weichun Wang

Chao Wang

Ye Ren

Jiang Zhu

Zhigang Hu

Jingxiong Wang

Wei Wang

Yuan Zhang

Lin Shen

Weiwei Cai

Binghua Luo

Xiaoyang Wu

Guangkai Ding

Junyan Tong

Cheng Ma

Mingyao Zhou

Li Wang

Jie Li

Min Ye

Peng Lin

Peng Wang

Wencong Zhang

Yi Niu

Hui Su

Jianxin Meng

Hui Gong

Tian Huang

Jun Che

Xinxin Nie

Xiaochen Quan

Jianhua Chen

Haiming Sun

Kui Zhang

Tao Fu

Lulu Zhou

Weijie Chen

Xing Su

Jiajun Shen

Tao Song

Xiang Tan

Yangqin Li

Zhongxian Xie

Shengsen Zhang

Ping Yang

Cheng Qin

Zuozhou Pan

A deep neural network operation method and apparatus are provided. The method comprises: obtaining an input feature map of a network layer; displacing respectively, according to a preset displacement parameter, each of channels of the input feature map of the network layer along axes, to obtain a displaced feature map, wherein the preset displacement parameter comprises displacement amounts of the channel in the axes; and performing convolution operation on the displaced feature map with a 1×1 convolution kernel to obtain an output feature map of the network layer. The operation efficiency of the DNN can be improved through the above method.

Deep neural network operation method and apparatus

An operation method and apparatus for a network layer in a Deep Neural Network are provided. The method includes: acquiring a weighted tensor of the network layer in the Deep Neural Network, the weighted tensor comprising a plurality of filters; converting each filter into a linear combination of a plurality of fixed-point convolution kernels by splitting the filter, wherein a weight value of each of the fixed-point convolution kernels is a fixed-point quantized value having a specified bit-width; for each filter, performing a convolution operation on input data of the network layer and each of the fixed-point convolution kernels, respectively, to obtain a plurality of convolution results, and calculating a weighted sum of the obtained convolution results based on the linear combination of the plurality of fixed-point convolution kernels of the filter to obtain an operation result of the filter; determining output data of the network layer.

Operation method and apparatus for network layer in deep neural network

Embodiments of the present application provide a navigation method, device and system. The method is applicable to a server and comprises: receiving current position characteristic information sent by a mobile terminal, and determining a position of an area matching the current position characteristic information based on the current position characteristic information and a locally stored database, and taking the determined position as an initial position; receiving target position characteristic information sent by the mobile terminal, determining a position of an area matching the target position characteristic information based on the target position characteristic information and the database, and taking the determined position as a target position; and determining a first route from the initial position to the target position and displaying the first route on an electronic map of the mobile terminal. Embodiments of the present application can provide users with accurate navigation information and improve user experience.

Navigation method, device, and system

An apparatus for acquiring an image includes an image sensor, a light compensator, and a light filter assembly. The image sensor is on a light output side of the light filter assembly, and is configured to generate and output a first image signal and a second image signal by a plurality of exposures. The first image signal is generated according to a first preset exposure and the second image signal is generated according to a second preset exposure, and they are two of the plurality of exposures. The light compensator includes a first light compensation apparatus, the first light compensation apparatus is configured to perform near-infrared light compensation, and the near-infrared light compensation is performed in at least part of an exposure period of the first preset exposure but is not performed in an exposure period of the second preset exposure.

Apparatus for acquiring image and method for acquiring image

An anti-counterfeiting face detection method, device and multi-lens camera are disclosed. The anti-counterfeiting face detection method comprises: acquiring a depth image, an infrared image and an RGB image by using a TOF camera and an RGB camera; analyzing the RGB image through a preset face detection algorithm to determine an RGB face region of a face in the RGB image and position information of the RGB face region; determining a depth face region of the face in the depth image and an infrared face region of the face in the infrared image based on the position information of the RGB face region; determining that the face passes the detection when the depth face region, the infrared face region and the RGB face region meet corresponding preset rules respectively.

Anti-counterfeiting face detection method, device and multi-lens camera

A multi-spectrum based image fusion apparatus is disclosed, which includes a light acquisition device, an image processor, and an image sensor having five types of photosensitive channels. The five types of photosensitive channels including red, green and blue RGB channels, an infrared IR channel and a full-band W channel. The light acquisition device acquires target light corresponding to incident light. The image sensor converts the target light into an image signal through the RGB channels, the IR channel and the W channel. The image processor analyzes the image signal into RGB color signals and a brightness signal, and fuses the RGB color signals and the brightness signal to obtain a fused image. The collection of the channels based on which the RGB color signals and the brightness signal are obtained includes the five types of photosensitive channels.

Multi-spectrum-based image fusion apparatus and method, and image sensor

A deep neural network training method and apparatus and a computer device are provided. The deep neural network training method includes: obtaining task attributes of nodes in a current network layer in a tree-like network topology (S); performing cluster analysis on the nodes in the current network layer based on the task attributes of the nodes in the current network layer and extracting a common part of task attributes of multiple nodes in a same category as a task attribute for a parent node of the multiple nodes (S); training a network parameter of each parent node based on a task attribute of this parent node (S); and determining that training of a deep neural network corresponding to the tree-like network topology is completed, after completion of training of all nodes in all network layers (S). The operation efficiency of deep learning can be improved through this solution.

Deep neural network training method and apparatus, and computer device

Embodiments of methods and apparatuses for object detection and of computer devices are disclosed. The method for object detection includes: acquiring an image to be detected that is captured by an image capturing means; inputting the image to be detected into a fully convolutional neural network obtained by training to generate an object upper-vertex confidence distribution diagram, an object lower-vertex confidence distribution diagram, and an object upper-and-lower-vertex correlation diagram for the image to be detected; for the object upper-vertex confidence distribution diagram and the object lower-vertex confidence distribution diagram respectively, determining upper-vertex objects and lower-vertex objects in the image to be detected by using a preset object determination method; for each first vertex, calculating a correlation value of a connection line connecting the first vertex object and each of second vertex object respectively by mapping the upper-vertex objects and the lower-vertex object onto the object upper-and-lower-vertex correlation diagram; and based on the correlation values, determining a connection line having a maximum correlation value as a specified object by matching the upper-vertex objects and lower-vertex objects. The accuracy of object detection can be improved through the present solution.

Target detection method and apparatus, and computer device

A surveillance method, apparatus, system, electronic device and computer-readable storage medium are provided. In the surveillance method, a non-visible light image and a target image are obtained, wherein the target image is generated from a visible light signal captured during a capture period of the non-visible light image. The method then detects whether an object is present in the non-visible light image. When an object is detected in the non-visible light image, a second location area of the object in a visible light image is determined according to a first location area of the object in the non-visible light image, such that surveillance of the object is implemented based on the visible light image, wherein the visible light image is an image determined based on the target image.

Monitoring method, apparatus and system, electronic device, and computer readable storage medium

Embodiments of the present application provide a method, apparatus, and electronic device for eliminating distortion of a distorted image. Side information components of a distorted image are generated. The distorted image is resulted from image processing on an original image. Side information components represent distortion features of the distorted image with respect to original image. Distorted image color components of the distorted image and the side information components are input into a pre-established convolutional neural network model for convolution filtering to obtain distortion-eliminated image color components. The convolutional neural network model is obtained through training based on a preset training set. The training set comprises original sample images, distorted image color components of multiple distorted images corresponding to each of the original sample images, and side information components of each of the distorted images. Thereby, a CNN-based distortion elimination process different from related art is provided.

Image distortion correction method and device and electronic device

A depth measuring method and system applicable to a first binocular camera having a zoom lens is provided. The method includes: obtaining a current depth of a target object (S); determining a focus with which the current depth is measured as a current focus (S); determining, according to the preset correspondence between depth ranges and focuses, a current reference focus corresponding to a current reference depth range; wherein, the current reference depth range is a depth range in which the current depth falls (S); determining whether the current focus is the same as the current reference focus; (S); if the current focus is the same as the current reference focus, determining the current depth as the target depth of the target object (S); or if the current focus is not the same as the current reference focus, adjusting the current focus to the current reference focus, measuring a current depth of the target object with the adjusted current focus (S), and proceeding to the operation (S) of determining, according to preset correspondence between depth ranges and focuses, a current reference focus corresponding to a current reference depth range. An object in various depth ranges is measured with a varying focus. The accuracy of the depth measurement of the target object is thus improved.

Depth measuring method and system

The present application discloses a method and an apparatus for displaying an image based on privacy masking, which belong to the field of video monitoring. The method includes: acquiring a to-be-masked original image provided by an image capture terminal and privacy masking area information of the original image; wherein, the privacy masking area information is obtained by identifying the original image or is obtained from the image capture terminal and indicates an area in the original image which is required to be masked; determining whether the original image is required to be privacy-masked based on whether privacy unmasking permission information is stored; if the original image is required to be privacy-masked, performing privacy-masking on the original image based on the privacy masking area information to obtain a masked image, and displaying the masked image. In this way, the image capture terminal is only required to provide an original image without providing the original image and the masked image, so the costs of both storage and transmission are low, saving the storage resources and network resources of the image capture terminal.

Method and apparatus for image display using privacy masking

Embodiments of the present application provide a method and apparatus for determining a trajectory of a human target. The method includes: extracting a target feature of a to-be-tracked human target in a to-be-processed image as a to-be-searched target feature; searching for an acquisition attribute corresponding to the to-be-searched target feature based on a pre-established correspondence between target features and acquisition attributes; determining a trajectory of the to-be-tracked human target based on the found acquisition attribute. It can be seen that in this solution, a face image is not required for determining a trajectory of a person. Even if the acquired face image is not clear, the accuracy of determining the trajectory of the person will not be reduced. Therefore, by the solution of the present application, the accuracy of determining the trajectory of the person is improved.

Method and device for determining path of human target

The embodiments of the present application provide an image segmentation method, an image segmentation apparatus, and a fully convolutional network system. The method includes: acquiring a target image to be processed; acquiring image feature data of the target image; inputting the image feature data into a pre-trained target network far image segmentation to obtain an output; wherein the target network is a fully convolutional network comprising a hybrid context network structure, and the hybrid context network structure is configured to extract a plurality of reference features at a predetermined scale and fuse them into a target feature that matches a scale of a target object in a segmented image; and wherein the target network is trained with sample images containing target objects at different scales; and obtaining an image segmentation result for the target image based on the output. With this technical solution, the effectiveness of segmentation of target objects of different sizes in the image can be improved while ensuring a large receptive field.

Image segmentation method, apparatus, and fully convolutional network system

A method and apparatus for acquiring people flow information, and a method and apparatus for processing people flow information are provided. The method for acquiring people flow information comprises: detecting entering and leaving of a target scene by people; determining specific attribute data of people flow entering and leaving the target scene, wherein, the specific attribute data includes at least a target number of people, among the detected people, which enters and leaves the target scene at the same time; and performing an operation of acquiring people flow information about the target scene, wherein, the operation of acquiring people flow information about the target scene includes at least: selecting, from a plurality of first-type counting modules, a first-type counting module corresponding to a predetermined number range of people within which the target number falls, and increasing a count value of the first-type counting module by 1, wherein, each first-type counting module corresponds to one of predetermined number ranges of people. The purpose of acquiring people flow information having a higher value in terms of business analysis, deep data mining and so on can be achieved by the solutions above.

Passenger flow information collection method and apparatus, and passenger flow information processing method and apparatus

Growing community of inventors

Hangzhou, China

Shiliang Pu