Detecting and tracking method, device and equipment of target object in video

Abstract

The invention discloses a detecting and tracking method, device and equipment of a target object in a video. The method comprises a step of inputting a continuous video frame into a convolutional neural network obtained by training in advance, wherein the convolutional neural network at least comprises a set number of shared convolutional layers and region proposal network layers, a step of extracting features of the continuous video frames by using the shared convolutional layers to obtain feature mapping maps corresponding to different video frames, a step of determines a target area associated with the target object according to the feature mapping maps by using the region proposal network layers, and a step of detecting the position and running trajectory of the target object in the continuous video frames based on the target area. According to the method, the detection and tracking are unified by using a convolutional neural network model, the amount of calculation is reduced, theproblem of detecting multiple poses and multiple angles of view of the target can be solved, the target detection rate is improved, and the false detection rate is reduced.

Description

technical field [0001] The present application relates to the technical field of machine vision, and in particular to a method, device and equipment for detecting and tracking a target object in a video. Background technique [0002] At present, the detection and recognition technology of dynamic target objects in video (such as: vehicles, pedestrians, etc. in video) is widely used in many fields such as transportation, security monitoring, and home entertainment. [0003] In the prior art, in order to extract a dynamic target object in a video, a background modeling and foreground detection (Background Generation And Foreground Detection) method is usually used to obtain a moving foreground, thereby obtaining a related target object. [0004] However, the above methods in the prior art are greatly affected by environmental factors such as illumination, which may cause more false detections, and may miss detections for stationary objects. Contents of the invention [0005...

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Problems solved by technology

[0004] However, the above-mentioned methods in the prior art are greatly affected by environmental factors such as illumination, which may cause more false detections, and may miss detection of stationary targets.

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