英文国际会议讲稿

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大家上午好！今天我汇报的主题是：基于改进型LBP算法的运动目标检测系统。运动目标检测技术能降低视频监控的人力成本，提高监控效率，同时也是运动目标提取、跟踪及识别算法的基础。图像信号具有数据量大，实时性要求高等特征。随着算法的复杂度和图像清晰度的提高，需要的处理速度也越来越高。幸运的是，图像处理的固有特性是并行的，尤其是低层和中间层算法。这一特性使这些算法，比较容易在FPGA等并行运算器件上实现，今天汇报的主题就是关于改进型LBP算法在硬件上的实现。

good morning everyone.

My report is about a Motion Detection System Based on Improved LBP Operator. Automatic motion detection can reduce the human cost of video surveillance and improve efficiency [?'f??(?)ns?]，it is also the fundament of object extraction, tracking and recognition [rek?g'n??(?)n]. In this work, efforts ['ef?ts]were made to establish the background model which is

resistance to the variation of illumination. And our video surveillance system was realized on a FPGA based platform.

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目前，常用的运动目标检测算法有背景差分法、帧间差分法等。帧间差分法的基本原理是将相邻两帧图像的对应像素点的灰度值进行减法运算，若得到的差值的绝对值大于阈值，则将该点判定为运动点。但是帧间差分检测的结果往往是运动物体的轮廓，无法获得目标的完整形态。

Currently, Optic Flow, Background Subtraction and Inter-frame difference are regard as the three mainstream algorithms to detect moving object.

Inter-frame difference based method need not model ['m?dl] the background. It detects moving objects based on the frame difference between two continuous frames. The method is easy to be implemented and can realize real-time detection, but it cannot extract the full shape of the moving objects [6].

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在摄像头固定的情况下，背景差分法较为简单，且易于实现。若背景已知，并能提供完整的特征数据，该方法能较准确地检测出运动目标。但在实际的应用中，准确的背景模型很难建立。如果背景模型如果没有很好地适应场景的变化，将大大影响目标检测结果的准确性。像这副图中，背景模型没有及时更新，导致了检测的错误。

The basic principle of background removal method is building a background model and providing a classification of the pixels into either foreground or background [3-5]. In a complex and dynamic environment, it is difficult to build a robust [r?(?)'b?st] background model.

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上述的帧间差分法和背景差分法都是基于灰度的。基于灰度的算法在光照条件改变的情况下，性能会大大地降低，甚至失去作用。

The algorithms we have discussed above are all based on grayscale. In practical applications especially outdoor environment, the grayscales of each pixel are unpredictably shifty because of the variations in the intensity and angle of illumination.

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为了解决光照改变带来的基于灰度的算法失效的问题，我们考虑用纹理特征来检测运动目标。而LBP算法是目前最常用的表征纹理特征的算法之一。首先在图像中提取相邻9个像素点的灰度值。然后对9个像素中除中心像素以外的其他8个像素做二值化处理。大于

等于中心点像素的，标记为1，小于的则标记为0。最后将中心像素点周围的标记值按统一的顺序排列，得到LBP值，图中计算出的LBP值为10001111。当某区域内所有像素的灰度都同时增大或减小一定的数值时，该区域内的LBP值是不会改变的，这就是LBP对灰度的平移不变特性。它能够很好地解决灰度受光照影响的问题。

In order to solve the above problems, we proposed an improved LBP algorithm which is resistance to the variations of illumination.

Local binary pattern (LBP) is widely used in machine vision applications such as face detection, face recognition and moving object detection [9-11]. LBP represents a relatively simple yet powerful texture descriptor which can describe the relationship of a pixel with its immediate neighborhood. The fundamental of LBP operator is showed in Fig 1. The basic version of LBP produces 256 texture patterns based on a 9 pixels neighborhood. The neighboring pixel is set to 1 or 0 according to the grayscale value of the pixel is larger than the value of centric pixel or not. For example, in Fig1 7 is larger than 6, so the pixel in first row first column is set to 1. Arranging the 8 binary numbers in certain order, we get an 8 bits binary number, which is the LBP pattern we need. For example in Fig.1, the LBP is 10001111. LBP is tolerant ['t?l(?)r(?)nt] against illumination changing. When the grayscales of pixels in a 9 pixels window are shifted due to illumination changing, the LBP value will keep unchanged.

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图中的一些常见的纹理，都能用一些简单的LBP向量表示，对于每个像素快，只需要用一个8比特的LBP值来表示。

There are some textures , and they can be represent by some simple 8bit LBP patterns. PPT(7)

从这幅图也可以看出，虽然灰度发生了很大的变化，但是纹理特征并没有改变，LBP值也没有变化。

You can see, in these picture , although the grayscale change alot, but the LBP patterns keep it value.

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上述的算法是LBP算法的基本形式，但是这种基本算法不适合直接应用在视频监控系统中。主要有两个原因：第一，在常用的视频监控系统中，特别是在高清视频监控系统中，9个像素点覆盖的区域很小，在如此小的区域内，各个像素点的灰度值十分接近，甚至是相同的，纹理特征不明显，无法在LBP值上体现。第二，由于以像素为单位计算LBP值，像素噪声会造成LBP值的噪声。这两个原因导致计算出的LBP值存在较大的随机性，甚至在静止的图像中，相邻两帧对应位置的LBP值也可能存在差异，从而引起的误检测。

为了得到更好的检测性能，我们采用基于块均值的LBP算法。这种方法的基本原理是先计算出3×3个像素组成的的像素块的灰度均值，以灰度均值作为该像素块的灰度值。然后以3×3个像素块（即9×9个像素）为单位，计算LBP值。

The typical LBP cannot meet the need of practical application of video surveillance for two reasons: Firstly, a “window” which only contains 9 p ixels is a small area in which the grayscales of pixels are similar or same to each other, and the texture feature in such a small area is too weak to be reflected by a LBP. Secondly, pixel noise will immediately cause the noise of LBP, which may lead to a large number of wrong detection. In order to obtain a better performance, we proposed an improved LBP based on the mean value of “block”. In o ur algorithm, one block contains 9 pixels. Compared with original LBP pattern calculated in a local 9 neighborhood between pixels, the improved LBP operator is defined by comparing the mean grayscale value of central block

with those of its neighborhood blocks (see Fig.2).By replacing the grayscales of pixels with the mean value of blocks, the effect of the pixel noise is reduced. The texture feature in such a bigger area is more significant to be described by LBP pattern.

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运用LBP描述背景，其本质上也是背景差分法的一种。背景差分法应用在复杂的视频监控场景中时，要解决建立健壮的背景模型的问题。驶入并停泊在监控画面中的汽车，被搬移出监控画面的箱子等，都会造成背景的改变。而正确的背景模型是正确检测出运动目标并提取完整目标轮廓的基础。如果系统能定时更新背景模型，将已经移动出监控画面的物体“剔除”出背景模型，将进入监控画面并且稳定停留在画面中的物体“添加”入背景模型，会减少很多由于背景改变而造成的误检测。

根据前一节的介绍，帧间差分法虽然无法提取完整的运动目标，但是它是一种不依赖背景模型就能进行运动目标检测的算法。因此，可以利用帧间差分法作为当前监控画面中是否有运动目标的依据。如果画面中没有运动目标，就定期对背景模型进行更新。如果画面中有运动目标，就推迟更新背景模型。这样就能避免把运动目标错误地“添加”到背景模型中。

In practical application, the background is changing randomly. For traditional background subtraction algorithm the incapability of updating background timely will cause wrong detection. In order to solve this problem, we propose an algorithm with dynamic self updating background model. As we know, Inter-frame difference method can detect moving object without a background model, but this method cannot extract the full shape. Background subtraction method can extract the full shape but needs a background model. The basic principle of our algorithm is running a frame difference moving object detection process concurrently [k?n'k?r?ntli] with the background subtraction process. What’s time to update the background is according to the result of frame difference detection.

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运动目标检测系统特别是嵌入式运动目标检测系统在实际应用中要解决实时性的问题。比如每秒60帧的1024×768的图像，对每个像素都运用求均值，求LBP等算法，那么它的运算量是十分巨大的，为此我们考虑在FPGA上用硬件的方式实现。

If LBP algorithm is implemented in a software way, it will be very slow. FPGA have features of concurrent computation, reconfiguration and large data throughput. It is suitable to be built an embedded surveillance system. The algorithm introduced above is implemented on a FPGA board.

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这就是我们硬件实现的系统结构图。首先输入系统的RGB像素信号的滤波、灰度计算及LBP计算，得到各个像素块的LBP值。然后背景更新控制模块利用帧差模块的检测结果控制背景缓存的更新。区域判定模块根据背景差模块的输出结果，结合像素块的坐标信息，对前景像素块进行区域判定。

The structure of the system is showed in this figure. I n this system, a VGA signal is input to the development board. and the LBP pattern is calculated , Frame difference module also compares the current frame and the previous frame to determine whether there is a moving object in the surveillance vision.If the surveillance vision is static for a certain amount of frame, the background model will be updated.

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图中是LBP计算模块。图中所示的窗口提取结构可以实现3×3像素块窗口的提取。像素信号按顺序输入该结构，窗口中的数据就会按顺序出现在Pixel1- Pixel9这9个寄存器中，

从而在最短的延时内提取出相邻9个像素点的灰度值。行缓存的大小等于每一行图像包含的像素个数减1。将9个像素点的灰度值通过求均值模块，可以求出一个像素块的像素均值。

将像素块均值作为输入再次通过类似的结构，可以提取出3×3个相邻像素块的灰度值。这时行缓存的大小为每一行包含的像素块的个数减1。再用9个窗口的灰度值作为输入，用比较器阵列计算出最终的LBP值。

To achieve real time computation of the LBP, a circuit structure is put forward as showed in Fig.5. Two line buffers and nine resisters are connected in the way showed in the figure. Nine neighbor pixels are extracted with minimum ['m?n?m?m] delay, and the mean value of this block is calculated by the mean value calculate module which contains some adders and shifters. The mean values of the blocks are inputted to a similar structure and extracted in a similar way, and the LBP is calculated by the consequence LBP calculate module.

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求均值模块采用如图3-12所示的四级流水方式实现。在算法的设计过程中，需要求出的是3×3像素块中9个像素的均值。但是在硬件实现时，为了更合理地利用硬件资源，只计算剔除中心像素后的8个像素的均值。这样做可以在不对计算结果造成太大影响的情况下减少加法器的使用。而且在求均值的最后一级流水，除8运算比除9运算更容易实现。因为8是2的整数幂，除8运算只需要将各个像素的和右移3位。而除9运算在FPGA中需要专用的DSP模块来完成。

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如图所示，块均值计算模块计算出的8个块均值被图3-11中的窗口提取模块提取出来，并作为比较器阵列的输入，比较器的输出结果用0和1表示。最终的比较结果按一定的顺序排列，重新拼接成一个8位的二进制数，即LBP值。LBP计算电路没有采用流水结构，在一个时钟周期内就能得到计算结果。

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这个是在系统测试中，实现对多个目标的检测。

In this system test ,we achieve a multi-object detection.

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这个图是对动态背景更新的测试，在监控区域中划定一个目标区域，把一个静止的物体放置到目标区域中。在前3分钟内，系统会将其当做前景目标，矩形窗口会以闪烁的形式发出报警信号。3分钟过后，由于物体一直处于静止状态，系统检测到了10800个静止帧，于是更新背景模型。静止的物体被当做背景的一部分，此后窗口不再闪烁。经验证，该系统能够正确实现背景模型更新算法。

This is the test for the auto background update. We put a statics object in the surveillance area，at the beginning this is trusted as a moving object . after 3 minutes , the system receive ten thousand static frames ,and then update the background model. Then this object is regard as a part of the background.

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此外为了验证系统对室外光照变化抑制能力，我们选取了大量有光照变化，并且有运动目标的视频对系统进行了测试。

In order to verify the resistance to the varation of illumination , a certification experiment is designed, and the ROC curves of the two algorithms based on LBP and grayscale are plotted and compared. A number of short video clips with shifty and fixed illumination, including positive

samples with moving objects and negative samples without moving objects .

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测试平台如图所示。用一台PC机作为测试信号的输出源，然后在PC机中播放视频，并将视频VGA信号发送给运动目标检测系统，模拟真实的监控环境。FPGA将输入信号和区域边框图形相叠加后在LCD上显示。

The picture of the certification experiment is showed in this picture . A PC acts as the source of the test signal which is input to the FPGA in the form of VGA. Passing through the FPGA board, video signal is displayed on a LCD screen.

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并最终描绘了系统的ROC特性曲线。在没有光照强度变化的情况下，采用基于灰度的运动目标检测算法的性能略优于基于LBP值的运动目标检测算法，两种算法都能取得较好的检测效果。但是在图5-15中（测试集2），也就是在光照强度变化的情况下，画面整体灰度发生较大的改变，基于灰度的检测算法的性能大幅度下降，接近于失效。而采用LBP值的检测算法却能维持较好的性能。可见基于LBP的检测算法对抑制光照强度变化造成的误检测有较好的效果。

This two figure are the ROC curves of the experiments using our algorithm and traditional grayscale-based algorithm . We can see in the Fig.1 which corresponds to the condition with fixed illumination, the performance of the grayscale-based algorithm is slightly better than these of LBP-based algorithm, they can both detect moving object effectively. But in Fig.2 which corresponds to the condition with shifty illumination, grayscale based algorithm deteriorates drastically and nearly lose efficacy['ef?k?s?]. But the improved LBP algorithm still keeps a good performance.

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谢谢大家！

Thanks for your attention

英语模拟国际会议讲稿

英语模拟国际会议讲稿 Company Document number：WUUT-WUUY-WBBGB-BWYTT-1982GT

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