Monitoring video multi-target tracking method based on deep learning

Abstract

The invention discloses a monitoring video multi-target tracking method based on deep learning. The method includes the steps of firstly encoding a video, extracting an image sequence from the encoded video, then preprocessing and inputting images into a trained Faster R-CNN network model, the Faster R-CNN network model extracting target location information and target spatial features from corresponding layers of the network; inputting the target location information and the target spatial features into an LSTM network, and predicting locations of targets at a next moment; and subjecting the spatial features of the targets to a fusion method to obtain fusion features of the targets at the next moment, adding different weights through the location similarity and spatial feature similarity to obtain the final similarity, and then judging the corresponding relationship of the multiple targets detected at the current moment and the multiple targets in a tracking state at the previous moment. By target tracking, the method of the invention can reduce the missed detection rate of multi-target tracking, improve the accuracy of multi-target tracking, and solve the target occlusion problem in a short time in the tracking process.

Description

technical field [0001] The invention belongs to the technical field of target tracking, and more specifically relates to a multi-target tracking method for surveillance video based on deep learning. Background technique [0002] The construction of safe cities and the popularization of high-definition cameras have produced massive surveillance videos. It is very time-consuming and difficult to collect clues from massive video image data by manpower alone, and it is difficult to identify targets due to human factors such as visual fatigue. Nothing goes wrong. With the development of visual computing, from machine learning to deep learning, computers can understand the information in videos more intelligently, and video intelligent analysis systems have emerged as the times require. The video intelligent analysis system extracts the moving target of each frame by analyzing the continuous image sequence, determines the target relationship in adjacent frames through tracking te...

AI Technical Summary

Problems solved by technology

A common way to solve this problem is to divide the target into multiple spatial regions, and set up a tracker for each spatial region. When it is blocked, there are departmental trackers that can continue to track, so there will be multiple trackers running at the same time, resulting in Tracking is too slow

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