本文選題：多目標跟蹤 + 自適應分塊��； 參考：《山東大學》2017年碩士論文

【摘要】：隨著計算機硬件和多媒體技術的發(fā)展,以及各國政府和民眾對安防的高度重視,智能視頻監(jiān)控的應用變得越來越廣泛,而多目標跟蹤技術作為智能視頻監(jiān)控領域最基本的核心技術,具有重要的研究意義和廣闊的應用前景,受到來自世界各地的學術界和工業(yè)界科研人員的普遍關注和研究。目前,多目標跟蹤技術的研究取得了長足進步,但仍存在許多難題需要解決,如復雜的跟蹤場景、非剛體目標的姿態(tài)變化、目標遮擋以及跟蹤的實時性等。本文針對復雜場景下存在目標遮擋、表觀變化以及相似目標的問題,對多目標跟蹤進行了研究,主要研究內(nèi)容及成果為:(1)介紹了多目標跟蹤的基本理論。對貝葉斯理論框架下的卡爾曼濾波和粒子濾波的基本原理做了簡單介紹,并分析了算法的優(yōu)缺點。介紹了均值漂移算法和模糊C均值算法的基本原理,并研究了算法的基本步驟。(2)在對多個目標進行跟蹤過程中經(jīng)常存在遮擋、相似目標的情況,為此研究了一種基于自適應分塊的粒子濾波多目標跟蹤方法。該方法根據(jù)目標的灰度分布進行自適應分塊,提高遮擋情況下準確跟蹤多目標的能力;在粒子濾波跟蹤時,利用均值漂移和模糊C均值聚類獲取每個目標對應的粒子群,得到目標最優(yōu)狀態(tài)估計;引入加權Bhattacharyya距離計算子塊的匹配度,考慮了子塊可靠性對粒子權重的影響。(3)為了解決多目標跟蹤過程中還經(jīng)常存在的相似目標相互遮擋以及目標表觀變化問題,提出了一種基于自適應分塊的多特征融合多目標跟蹤方法。該方法在上一方法的基礎上加入了多特征融合策略,融合顏色直方圖和HOG特征對目標進行描述;在粒子濾波跟蹤時,依據(jù)子塊可靠性以及粒子的空間分布及時調(diào)整目標模型中子塊的權重;并且為減少過程中目標變化對跟蹤結果的影響,采取權重更新方法動態(tài)更新目標特征模型。實驗結果表明,該方法在多目標跟蹤過程中存在表觀變化、目標相似以及目標遮擋或者相似目標相互遮擋的復雜情況下,均能準確魯棒地跟蹤多個目標。
[Abstract]:With the development of computer hardware and multimedia technology, as well as the governments and people of various countries attach great importance to security, the application of intelligent video surveillance has become more and more widespread. As the most basic core technology in the field of intelligent video surveillance, multi-target tracking technology has important research significance and broad application prospects, and has been widely concerned and studied by researchers from academia and industry all over the world. At present, the research of multi-target tracking technology has made great progress, but there are still many problems to be solved, such as complex tracking scene, attitude change of non-rigid object, target occlusion and real-time tracking. Aiming at the problems of object occlusion, apparent variation and similar targets in complex scenes, this paper studies multi-target tracking. The main research content and result is: (1) the basic theory of multi-target tracking is introduced. The basic principles of Kalman filter and particle filter based on Bayesian theory are introduced, and the advantages and disadvantages of the algorithm are analyzed. This paper introduces the basic principle of mean shift algorithm and fuzzy C-means algorithm, and studies the basic steps of the algorithm. In this paper, a particle filter multi-target tracking method based on adaptive blocking is studied. According to the gray level distribution of the target, the method adaptively divides blocks to improve the ability of accurately tracking multiple targets under occlusion, and obtains the corresponding particle swarm of each target by means of mean shift and fuzzy C-means clustering in particle filter tracking. The optimal state estimation of the target is obtained, and the weighted Bhattacharyya distance is introduced to calculate the matching degree of the subblock. The influence of sub-block reliability on particle weight is considered. A multi-feature fusion multi-target tracking method based on adaptive block is proposed. Based on the previous method, a multi-feature fusion strategy is added to describe the target with color histogram and HOG feature. According to the reliability of the subblock and the spatial distribution of the particle, the weight of the neutron block of the target model is adjusted in time, and in order to reduce the influence of the target change on the tracking result, the target feature model is dynamically updated by the weight updating method. The experimental results show that the proposed method can track multiple targets accurately and robustly in the case of the apparent changes in the process of multi-target tracking, the similarity of targets, and the complexity of object occlusion or mutual occlusion of similar targets.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TN948.6

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