System for tracking infrared human body target based on corpuscle dynamic sampling model

Abstract

The invention provides an infrared human body target tracking system based on a particle dynamic sampling model. The system includes an infrared thermal imager, an image acquisition device, a central processing unit, and a video display device; the image acquisition device collects the video data stream of the infrared thermal imager and Send it to the central processing unit, and the central processing unit forms the infrared video data into a continuous image sequence and tracks the image sequence. The tracking method is realized through random particle sampling and migration control. Taking each human target as an object, random sampling particles are regenerated according to the observed particles of each target, and redundant particles are removed according to the set judgment rules for invalid sampling particles; The state of the target in the pool is judged; the tracking result processed by the central processor is sent to the video display device for display; the object-oriented technology is used to encapsulate the complex target state of the human body, which overcomes the inability of the traditional target tracking method to realize the infrared image sequence The problem of reliable tracking of human targets in .

Description

technical field [0001] The invention relates to a tracking system of a human body target, in particular to an infrared human body target tracking technology based on a particle dynamic sampling model and an object-oriented method. Background technique [0002] Compared with visible light imaging, the advantages of infrared imaging are self-evident: no auxiliary light source is required, and it can work around the clock in almost any weather conditions and environments. With the development of infrared device manufacturing technology, especially the uncooled focal plane infrared CCD manufacturing technology and the decline in price, the application field of infrared imaging system has been rapidly expanded. [0003] The human target is the most active and valuable element in the scene, so the application of tracking technology for complex non-rigid targets such as human targets in infrared images is very wide. The main application fields include: infrared aiming system, infr...

AI Technical Summary

Problems solved by technology

[0008] Due to the high subjective randomness of the human target, the target may change the speed, direction, form and path of the movement at any time when the observer cannot predict it. That is, in most cases, the movement of the human body has no rules to follow, and the movement model with a fixed dynamic system cannot be used to describe the movement laws of the human body

This makes some tracking methods based on motion law assumptions, such as the Kalman filter method, unable to effectively track human targets.

[0009] (3) Defects of infrared imaging itself

[0010] Since the infrared image is a grayscale image without the color information of the target, the tracking method based on color features such as skin color and clothing, which is widely used in visible light images, cannot be used; due to the lack of texture details of the human target in the infrared image, the local texture expression of the target does not have Good identifiability, some non-rigid object tracking methods based on texture similarity calculation in the visible light image target tracking method, such as the traditional Mean Shift tracking method, conduct similarity analysis through the Bhattacharyya coefficients of the front and rear two regions, and maximize the Bhattacharyya coefficient The tracking window area is used as the matching area, which is not applicable in the infrared human target tracking process, because the gray histogram features of different targets are very close, and the feature quantity used to judge the difference between different targets is relatively small. Less, using the Bhattacharyya coefficient of the gray histogram for similar matching has a high possibility of mismatching

[0011] (4) Independence of human target detection algorithm

[0012] In addition to the unreliability of the detection algorithm caused by the complex changes in the posture of the human target, the image of the human target is often incomplete and broken due to the interference of the background, and has completely lost the basic characteristics of the human target. Even if the detection algorithm is 100% Reliable, human targets cannot be identified at this time, so a tracking solution based entirely on detection algorithms is not feasible

[0013] The tracking of infrared human targets at home and abroad is based on the assumption of the state of the target: such as assuming that the target is moving at a constant speed, assuming the posture of the target is an upright state (pedestrian), assuming that the camera is stationary, etc., using Kalman filter, Particle filter or Mean Shift method for tracking based on state prediction method and similarity analysis, the application occasions are very limited and the tracking results are not ideal

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