Study On Human Detection And Tracking Methods In Infrared Images

Infrared-image, especially the far-infrared image has prominent advantage comparing with the visible-image. As a result of thermal based imaging, infrared image is independent of external luminous qualification and is able to see the objects we interested in through darkness and frog, while the visible optical imaging is incapable to do this.Human detection and tracking are very important issues we concern with all along for humans are the most active and the most valuable factors in many occasions. Human detection and tracking system based on infrared imaging can almost work in any environment and all-weather conditions. It is irreplaceable in some situations and has vast potential applications in many aspects.However, this task is very challengeable from the technical viewpoint. It is involved with infrared image segmentation, human features extraction, description and classification, no-rigid complex objects tracking, etc. The main difficulties we have to confront firstly is the robust problem of the image segmentation algorithm which is caused by the drastic fluctuation of infrared image's performance parameters because of the discrimination of different infrared imaging device , the environment changes and the intrinsic properties of infrared image, such as low resolution, narrow dynamic range and poor image quality. Secondly, because of infrared image is gray image, there are no color information available and some other drawbacks such as image blur, low textures, whereas human are non-rigid objects, their postures and appearances are complicated and changeable and their sizes are various, it is very difficult to extract and descript human features in infrared image effectively and to distinguish them from disturbances. Further more, human's movements are very subjective and unbending; there are no routines to recapitulate them. Meanwhile, the movements also accompany with human postures and appearances transformation, the tracking methods used in rigid objects are not suitable for human tracking. One misfortune after another, being short of features for tracking, some excellent human tracking algorithms based on color information and textures can't work efficiently for infrared human tracking.After studying on current human detection and tracking methods both in infrared images and visible images and analyzing on human imaging properties, the author presented an integrated solutions described as follows: (1) For general infrared images, we proposed a threshold selection method for image segmentation based on image histogram multi-clustering analysis proceeded from the infrared imaging mechanism. Specifically, we used K-means clustering centers algorithm to achieve the fast threshold calculation. For polarity-reversed infrared image segmentation we just adopted the traditional Mean Shift image segmentation algorithm. For occluded human segmentation, we proposed a relative position Mean Shift clustering algorithm in two-dimensional space applied to the object's binary template, and we used the similar idea of this algorithm in subsequent human tracking system.(2) After image segmentation, we verified the valid candidates by the object's binary template properties. For some half-baked and fragmented templates we proposed an enhanced candidates selection method. Carry out an image distance transform process on the surplus templates after the first candidates choosing process, then using the distance restriction to find the potential candidates corresponding regions. A second threshold calculation algorithm is invoked if necessary.(3) The feature extraction and description scheme of candidates are based on local area generated by grid division. There are two methods for doing this: Local Gradient-Orientation Histogram based feature description methods and Local Maximal Oriented Energy Histogram based feature description methods. We adopted RBF-SVM classifiers to predicate whether candidates were human or disturbance.(4) For human tracking, we proposed a tracking algorithm based on Particles Mean-Shift Migration algorithm. This algorithm is independent on any complicated movement manner of human. When designing the tracking system, we used the Object Oriented methods to encapsulating human features, the tracking problem's substance was revealed. By introducing the finite-state machine technology and combining with the human detection method, the problems of new human coming, human hiding, occluding and disappear were handled properly.All the algorithms proposed in the scheme have been realized by the author and are validated by experimental results. The"Human Detection System for Infrared Images"and the"Object Oriented Human Tracking System in Infrared Videos"are designed based on the scheme by system integration; the performances of them are encouraging in practical application.