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Study on Human Target Detection and Tracking Based on Latent SVM

Author: HuZhenBang
Tutor: CaiZhiHua
School: China University of Geosciences
Course: Earth Science Information Engineering
Keywords: Latent SVM cascade Latent SVM mean-shift Differential evolution self-orgnized mapping background subtraction RJMCMC
CLC: TP391.41
Type: PhD thesis
Year: 2013
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Abstract


Human target detection and tracking is an important research area with many applications such as Intelligent Transportation System, intelligent video surveillance, advanced human-machine interface, intelligent robot, video analysis and electronic entertainment. With the development of the Internet of Things, the system to solve human target detection and tracking is more popular.In this thesis, the problem is decomposed into image detection, background subtraction, image tracking and path optimization which are discussed separately.Latent SVM (support vector machine) is the major research method for image detection in this thesis. In the training process of Latent SVM, the training image samples are clustered into different subsets according to the aspect ratio. To adapt to the different observation angle of view, different component of the model are trained from these subsets synthetically. Besides this, a futher analysis is adaptived for the SVM model to automaticly extract latent variable. Latent SVM is better and more complicate than general SVM algorithm. The latent variable has displacement and appearance information. A Latent SVM model of human can has dozens of latent variable. These latent variables could be considered as locally hunman object characters, such as head, body, arms and legs.The muil-component and latent variables make the Latent SVM be the one of the best image detection algorithms. In this thesis, the research about Latent SVM involved bath training and detection progress.As an assisted method, background subtraction algorithm could be utilized in the system to improve the accuracy and speed. The goal of background separation algorithm is effectively remove the background area of the video image sequence such as tresss waving in the wind, fans turning, shadow of the moving target, et cetera. The histograms of oriented gradients (HOG) pyramid of the detected images are utilized in the Latent SVM to detect the human target. Detection time consuming is increasing with the scanning area. For the real-time video image detection system, information of the time adjacent images could be utilized to quickly remove out most of the background region and improve detection speed. The scanning area is only focus on the foreground moving region, and the detection error rate of is greatly reduced. In this thesis, a variety background subtraction algorithm is introduced, such as multivariate gaussian mixture model algorithm, coding method, and self-organizing mapping background separation algorithm. Integrated the advantages of these methods, an improved algorithm is proposed.Compared with the image detection algorithm, image tracking algorithm needs to find out the location of the target and the track of the target movement path. Image tracking algorithm has certain continuity and automaticity, and be able to make up some missing detection of image detection algorithm. An improved Mean-Shift image tracking algorithm is introduced in this thesis. The improved algorithm is combined with background subtraction result which could help to get a better tracking position and speed. Inputing the initial track area, the algorithm can automatically complete tracking task.When the tracking target conceal by the other target or background, image tracking algorithm would inevitably produce tracking error or missing.Tracking path optimization algorithm could help to make up this problem. Combined with location and appearance information of the tracking targets, a function could be desiged and optimal tracking path. An improved reversible jump Markov chain Monte Carlo-RJMCMC algorithm is introduced in this thesis. The improved algorithm can get better performance in high detection accuracy. Before using tracking path optimization algorithm, the first step is the background separation to get foreground region, then Latent SVM is applyed to detect human targets in the foreground area. And finally, the initial tracking path is obtained from image tracking algorithm. These processes can maximize reduce error detection rate, and greatly simplifies the tracking path optimization problem.In this thesis, complete human target detection and tracking system prototype is designed. Consider the defect and deficiency in each module, an improvement research is conducted. The main contributions of this thesis are as follows.1) Latent SVM model training algorithm is improved. Human target images have large variability and the initialization of latent variables in the latent SVM model is very important. In the original Latent SVM model training process, a simple classification template is trained from sample HOG features by SVM algorithm. Then a greedy algorithm is conducted with the classification template to obtain the hidden variables. In order to get a better training model, a new image segmentation algorithm based on Mean-Shift and differential evolution algorithm is proposed to generate better latent variable. The propose method taking into account texture distribution feature of the positive sample set image and automatically search local characteristics of hidden variables for a better detection model and performance.2) The detection algorithm of Latent SVM is improved. The HOG pyramid of the detected images are utilized in the Latent SVM to detect the human target.The original detection algorithm of Latent SVM is conducted by convolution of the HOG pyramid and Latent SVM model. And the location and size of the target can be obtained from the convolution score and pyramid level.The cascade Latent SVM detection algorithm is a fast detection algorithm based on original algorithm. Firstly, PCA is applied to the sample set HOG features, and a dimension reduced HOG pyramid is obtained.The cascade Latent SVM detection algorithm utilize the dimension reduced HOG pyramid to make convolution with the Latent SVM model and only select appropriate loations with large convolution scores for further analysis.And the further analysis is the original Latent SVM detection within the special location. The cascade Latent SVM detection algorithm could fast filter out none human target area within the image.To further improve the performance the cascade Latent SVM detection algorithm, a dimension reduction algorithm based on LDA is introduced in this thesis. Besides, a new latent variables locally search algorithm is also introduced. Finally, in order to reduce the detection false alarm rate, a second decision model based on color similarity of the latent variables is constructed.3) An improved self-organizing map background separation algorithm is proposed. Classic multivariate gaussian mixture model and coding method both treat each pixel of the image as the basic processing unit. This kind of methods did not make a correlation processing between adjacent pixels, and sometimes can not well adapt to the changing of the scene, and the separation result may has high false alarm. Self-organizing map can effectively solve the information connection between each pixel in the scene, and has a better ability to adapt the scence changing. However, this method requires manual intervention to fixed code length and the code book can not modified according to the scene mutation. A new algorithm combined with coding method and self-organizing mapping is proposed in this thesis which builds association of adjacent pixels and enable variable-length code book. Finally, a improved algorithm based on this algorithm and SVM is introduced and applied in the fusion of infrared and color image data to effectively remove shadow.4) Mean-Shift and background subtraction algorithm are used together to track multiple people in image sequences. It has tow contributions. Firstly, the moving targets area is extracted effectively and the feature vector correlation value is utilized as the measure for the tracking accuracy. Secondly, a fast region modify progress is conducted based on the foreground-background segmentation result. The improved algorithm has better performance in terms of time consuming, robust and tracking accuracy than the conventional mean shift algorithm.5) An improved tracking path optimal algorithm is introduced is this thesis. When the tracking target conceal by the other target or background, image tracking algorithm would inevitably produce tracking error or missing. The human target detection and tracking system firstly utilized the background subtraction algorithm get the foreground area. Then, an improved cascade Latent SVM algorithm is implemented in the foreground area to get the human targets and checked by the color similarity model. The initial tracking path is obtained from the detection results and Mean Shift algorithm. These processes could make sure a high detection accurance. In this special situation, compared with original tracking path optimal algorithm, improved algorithm can get better perfoemance. The improved algorithm involved a simpler optimal function and simpler optimal stratiage. The simpler optimal stratiage drops out decrease, increase, add, delete optimal stratiage, only keep merge and splite to get a better performance. In summery, through analysis of the human target detection and tracking related algorithms, this thesis pointed out the drawbacks of the related algorithms. Based on the above-mentioned analysis, this thesis mainly studied:the training algorithm of Latent SVM, cascade Latent SVM detection algorithm, self-orgnized mapping background subtraction algorithm, mean-shift image tracking algorithm, RJMCMC multi-target tracking optimal algorithm. In addition, for each improved algorithms, the experimental study was conducted to validate its performance.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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