Beijing Kuangshi Technology Co., Ltd.

Pinhole (beijing) Technology Co., Ltd.

Megvii (beijing) Technology Co., Ltd.

Qizheng He

Chao Li

Gang Yu

Qi Yin

Muge Chen

Yuxiang Peng

Zeyuan Shang

This application provides a video monitoring method and process. The video monitoring method comprises: obtaining first and second video data of a scene being monitored; detecting at least one target object based on the first video data, and determining parameter information of the at least one target object in at least one frame of the first video data, the parameter information including a first position; determining, based on coordinate transforming relationship between the first and the second video data, a second position of the at least one target object in a corresponding frame in the second video data according to the first position; and extracting, based on the second video data, feature information of the at least one target object located in the second position, wherein orientations with which the first and the second video acquiring module acquire video data with respect to a ground plane are different.

Video monitoring method and video monitoring system

This application provides a video monitoring method and device. The video monitoring method includes: obtaining video data; inputting at least one frame in the video data into a first neural network to determine object amount information of each pixel dot in the at least one frame; and executing at least one of the following operations by using a second neural network: performing a smoothing operation based on the object amount information in the at least one frame to rectify the object amount information; determining object density information of each pixel dot in the at least one frame based on scene information and the object amount information; predicting object density information of each pixel dot in a to-be-predicted frame next to the at least one frame based on the scene information, the object amount information, and association information between the at least one frame and the to-be-predicted frame.

Video monitoring method and video monitoring device

A method and an apparatus for measuring a speed of an object are provided, the method comprising: acquiring, for each image frame of at least two image frames in an image sequence, image information of the image frame, wherein the image information comprises depth information; detecting at least one object in the image frame based on the depth information to obtain an object detection result for the image frame; tracking the at least one detected object based on the object detection result of each of the at least two image frames to obtain an object tracking result; and calculating the speed of the at least one detected object based on the object tracking result and a time difference between the at least two image frames.

Method and apparatus for detecting a speed of an object

Provided are a driving assistance information generating method and device, and a driving assistance system, which relate to a field of vehicle driving assistance technique. The driving assistance information generating method comprises: obtaining a depth image acquired by a depth camera and a scene image acquired by an imaging camera, the depth camera and the imaging camera being mounted on a vehicle in a manner of being registered and matched with each other, and a coverage of the depth camera and a coverage of the imaging camera being at least partially overlapped; detecting positions of respective objects appearing in the scene image by using the depth image and the scene image; and generating driving assistance information of the vehicle based on positions of the respective objects.

Driving assistance information generating method and device, and driving assistance system

An object detection method and an object detection apparatus are provided. The object detection method includes: mapping at least one image frame in an image sequence into a three dimensional physical space to obtain three dimensional coordinates of each pixel in the at least one image frame; extracting a foreground region in the at least one image frame; segmenting the foreground region into a set of blobs; and detecting, for each blob in the set of blobs, an object in the blob through a neural network based on the three dimensional coordinates of at least one predetermined reference point in the blob, to obtain an object detection result.

Object detection method and object detection apparatus

An object detection method and an object detection apparatus are provided. The object detection method comprises: mapping at least one image frame in an image sequence into a three dimensional physical space to obtain three dimensional coordinates of each pixel in the at least one image frame; extracting a foreground region in the at least one image frame; segmenting the foreground region into a set of blobs; and detecting, for each blob in the set of blobs, an object in the blob through a neural network based on the three dimensional coordinates of at least one predetermined reference point in the blob, to obtain an object detection result.

The present disclosure relates to a video monitoring method and a video monitoring system based on a depth video. The video monitoring method comprises: obtaining video data collected by a video collecting module; determining an object as a monitored target based on pre-set scene information and the video data; extracting characteristic information of the object; and determining predictive information of the object based on the characteristic information, wherein the video data comprises video data including the depth information.

Video monitoring method, video monitoring apparatus and video monitoring system

The present disclosure relates to a video monitoring method, a video monitoring system and a computer program product. The video monitoring method comprises: obtaining video data collected by a video data collecting apparatus; and based on pre-set scene information and the video data, performing determination and statistics of monitored objects in a scene to be monitored corresponding to the scene information.

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Beijing, China

Qizheng He