本文關鍵詞： 目標群 航母戰(zhàn)斗群 行為分析 行為識別 人群場景 多觀察點上下文 多觀察點統(tǒng)計直方圖 視頻描述子 從屬關系　出處：《華中科技大學》2016年博士論文　論文類型：學位論文

【摘要】：群體行為識別與分析是模式識別和計算機視覺領域的前沿課題,為公共場所視頻監(jiān)控、戰(zhàn)場實時分析等提供有效的技術手段。隨著計算機視覺技術的快速發(fā)展,基于圖像的目標檢測、識別等技術日趨成熟。然而,基于視頻的技術仍需進一步提高,特別是行為識別。群體行為識別是行為識別的一種,其場景比一般行為復雜。到目前為止,不同的目標群體很難利用固定的算法進行分析。相對單個目標或多個目標,群體行為是整體行為,目標與目標之間的上下文關系密切。通常具有以下特點:目標數(shù)量較多,運動環(huán)境復雜,速度快慢不一,密度高或相互之間遮擋嚴重等。目前,國內外學者對該課題研究不多。雖然近幾年取得了一些階段性的成果,出現(xiàn)了一些大型數(shù)據(jù)庫,但是不同課題組研究的切入點不同,整體研究處于比較分散的階段。本文的研究主要集中在兩類目標群。一是稀疏目標群(例如航母戰(zhàn)斗群);二是稠密目標群(例如人群)。本文以航母戰(zhàn)斗群為例研究了稀疏目標群;以人群為例研究了稠密目標群。針對群體目標的特點,本文的研究內容如下:首先,模擬了衛(wèi)星監(jiān)視中的航母戰(zhàn)斗群航行視頻。航母戰(zhàn)斗群的數(shù)據(jù)非常寶貴,模擬航母戰(zhàn)斗群隊形變化航行的意義重大。雖然目前的衛(wèi)星技術很難支持大范圍的視頻拍攝,但是模擬視頻可以驗證識別算法在任意時刻的有效性。為了盡可能真實模擬航母戰(zhàn)斗群的航行,本文分析了雷達偵察衛(wèi)星和光學偵察衛(wèi)星監(jiān)視航母戰(zhàn)斗群的可行性。提出利用三次Hermite插值函數(shù)規(guī)劃軍艦的軌跡,既可以保證規(guī)劃的軌跡函數(shù)二次可導,也能很好控制隊形變化過程中艦船之間的距離,防止發(fā)生碰船事件。最后,為了增強模擬航行的視頻真實性,采用“谷歌地球”中的航母和軍艦照片作為軍艦模板,動態(tài)海面作為背景,沿著設置好的軌跡生成航母戰(zhàn)斗群的模擬航行視頻。其次,在假設艦船目標已經(jīng)檢測出來的基礎上研究了航母戰(zhàn)斗群的隊形識別和行為分析。提出在阿基米德螺線上選取一系列觀察點,計算每個觀察點與航母戰(zhàn)斗群的上下文信息,形成了多觀察點上下文描述子,成功解決了旋轉和尺度不變性問題。建立了概率密度函數(shù)模型,將隊形的局部信息與全局信息有效融合,增強了算子的描述能力。該描述子的維度與軍艦數(shù)量無關,其識別性能對航母戰(zhàn)斗群的中心區(qū)域軍艦數(shù)量不敏感,符合航母戰(zhàn)斗群編隊的實際情況。提出了基于隱馬爾科夫模型的行為識別方法,并在不同的模擬視頻中驗證了算法的有效性。針對人群檢測,本文提出了一種新的局部區(qū)域描述子——多觀察點統(tǒng)計直方圖。在多個觀察點上進行徑向梯度變換,形成了一種局部區(qū)域的整體描述子。在不需要歸一化圖像尺度大小的情況下,能統(tǒng)一描述不同尺度的人群圖像塊。最后結合了快速目標框的方法進行人群檢測�；谀繕丝虻姆椒ㄈ巳簷z測不需要高斯金字塔,但卻可以利用少數(shù)目標框覆蓋大部分目標。針對形態(tài)變化較大的人群場景,提出了一種多標簽的分類器模型。人群行為種類繁多,類別之間關系復雜,傳統(tǒng)的分類器效率不高、效果不好。充分利用不同類別之間的從屬關系,提出了一種高效分類器模型。相比傳統(tǒng)分離器,本文的分類器具有完美的閉合解,可以同時高效處理多個類別。人群場景分類是一個多實例的問題,本文結合了深度卷積網(wǎng)絡和Fisher Vector(FV)編碼,構建了具有時空信息的視頻描述子,高效處理了多實例問題。以上方法均在充足的數(shù)據(jù)集和實驗下進行了驗證。實驗結果表明,本文提出方法大部分結果優(yōu)于主流方法。大部分數(shù)據(jù)集是實際拍攝的視頻數(shù)據(jù),提出的方法具有很強的實際應用價值。本文的方法可以視為一些基本的技術,具有相關領域潛在的實際應用價值,例如,事件檢測、行為識別等。
[Abstract]:Behavior recognition and analysis of the group is a leading research field of pattern recognition and computer vision, video surveillance for public places, provide effective technical means real-time battlefield analysis. With the rapid development of computer vision technology, image target detection based on recognition technology is becoming more and more mature. However, the video technology needs to be further improved based on the special is the behavior recognition. Group behavior recognition is a kind of behavior recognition, the scene is more complex than the general behavior. So far, different target groups are difficult to analyze using the fixed algorithm. Compared with single target or multiple targets, group behavior is the overall behavior context between the target and the target is usually close. The following characteristics: a large number of target motion in complex environment, the speed of a high density or mutual occlusion seriously. At present, domestic and foreign scholars on the subject. There is not much. Although in recent years has made some achievements, there are some large databases, but the starting point of different research group, the overall research in a relatively dispersed phase. This study focused on two types of target groups. One is the sparse target groups (e.g. carrier battle group); two is the dense target group (e.g. population). The aircraft carrier battle group as an example to study the sparse target group; population as an example to study the dense target group. According to the characteristics of the target groups, the research contents of this paper are as follows: firstly, the simulation of satellite surveillance of aircraft carrier battle groups sailing video carrier battle group data. Very valuable, simulation of the aircraft carrier battle group formation changes sailing of great significance. Although the satellite technology is difficult to support a wide range of video capture, but analog video recognition algorithm can be verified at any time for effectiveness. As far as possible to simulate navigation aircraft carrier battle group, this paper analyzes the radar reconnaissance satellite and optical reconnaissance satellite surveillance aircraft carrier battle group. The feasibility of proposed using three Hermite interpolation function planning ship trajectory, which can not only ensure the planning path function can guide two times, can well control the ship formation changes in the process of distance the ship, to prevent the occurrence of touch events. Finally, in order to enhance the authenticity of the video simulation of navigation, the "Google earth" in the aircraft and warships warship photos as template, dynamic sea as background, set up along the trajectory generation of aircraft carrier battle groups sailing simulation video. Secondly, based on the ship target detection has been assumed on the study on the analysis of formation recognition and behavior. The carrier battle group selected a series of observation points in Archimedes spiral, calculated for each observation point and the aircraft carrier battle group The context information, the formation of multi observation point context descriptor is solved successfully, rotation and scale invariance. A probability density function model, local information and global information formation and effective integration, enhance the operator description ability. The number of dimensions and warships the descriptor is independent of the recognition performance is not sensitive to the number of regional center Navy aircraft carrier battle group, in accordance with the actual situation of the aircraft carrier battle group formation. Put forward the behavior recognition method based on Hidden Markov model, and verify the effectiveness of the algorithm in the analog video. According to different crowd detection, this paper proposes a new local descriptor, multiple observation points of radial histogram. The gradient transform in a plurality of observation points, forming a whole a local descriptor. Without the need of normalized image size under the condition of uniform People describe image blocks at different scales. Finally the method of fast target frame for crowd detection. Detection method of target population box does not need Gauss in Pyramid based on, but can use a box cover most of the target object. According to the morphological changes of large crowd scenes, proposed a multi label classifier model. The crowd behavior types there are categories of the relationship between the complexity of the traditional classifier, the efficiency is not high, the effect is not good. Make full use of the dependencies between different categories, this paper presents an efficient classifier model. Compared with the traditional separator, the classifier has the perfect solution, at the same time, can handle multiple categories. The crowd scene classification is a multi examples of problems, combining the convolutional network and Fisher Vector (FV) encoding, construct the video descriptors with spatial and temporal information, efficient handling of multiple instances All the above problems. The method was validated in sufficient data sets and experiments. The experimental results show that this method is better than most of the results of mainstream method. Most of the data set is the actual shooting video data, the proposed method has strong practical application value. This method can be regarded as some of the basic technology, practical related areas of potential application value, for example, event detection, behavior recognition.

【學位授予單位】：華中科技大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41

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