本文選題：行人檢測(cè)　切入點(diǎn)：卷積神經(jīng)網(wǎng)絡(luò)　出處：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：行人檢測(cè)一直是機(jī)器視覺(jué)領(lǐng)域的研究熱點(diǎn)和難點(diǎn),其在智能監(jiān)控、智能交通和智能機(jī)器人等人工智能領(lǐng)域應(yīng)用越來(lái)越廣泛,比如在交通安全領(lǐng)域,利用行人檢測(cè)技術(shù)可以預(yù)判前方及附近是否有行人,若發(fā)現(xiàn)則立即采取緊急制動(dòng),這樣能夠有效避免車輛碰撞行人,減少人員傷亡。行人檢測(cè)不同于普通目標(biāo)檢測(cè),行人屬于非剛性目標(biāo),在現(xiàn)實(shí)生活中,行人穿著各式各樣、人體姿態(tài)千變?nèi)f化、所處背景復(fù)雜多變、光照不足以及行人之間相互遮擋等情形給這項(xiàng)工作帶來(lái)巨大的挑戰(zhàn)。前人提出了許多有效的行人檢測(cè)算法,其中最有代表性的是梯度直方圖(Histogram of Oriented Gradient,HOG)特征,但其在更為復(fù)雜的背景環(huán)境下檢測(cè)效果仍然不是很理想。近年來(lái),深度學(xué)習(xí)重新進(jìn)入人們的視角,其中深度卷積神經(jīng)網(wǎng)絡(luò)在模式識(shí)別方面更是取得了重大的突破,說(shuō)明了其在特征提取方面的優(yōu)越性。本文在充分研究行人檢測(cè)技術(shù)以及深度學(xué)習(xí)尤其是深度卷積神經(jīng)網(wǎng)絡(luò)模型的基礎(chǔ)上取得如下成果:(1)設(shè)計(jì)了基于區(qū)域卷積神經(jīng)網(wǎng)絡(luò)的行人檢測(cè)系統(tǒng)。針對(duì)傳統(tǒng)人工設(shè)計(jì)的特征提取復(fù)雜度高且難以有效表達(dá)復(fù)雜場(chǎng)景中的行人特征的問(wèn)題,本文采用深度卷積神經(jīng)網(wǎng)絡(luò)模型來(lái)進(jìn)行行人檢測(cè),該模型通過(guò)組合低層特征形成更加抽象的高層表示屬性類別或特征,進(jìn)而從樣本中提取魯棒性更強(qiáng)、更能刻畫(huà)圖像的特征向量。由于網(wǎng)絡(luò)模型層次較深,需要訓(xùn)練參數(shù)較多,而人工標(biāo)注行人的數(shù)據(jù)樣本較少,為了防止訓(xùn)練過(guò)程中的過(guò)擬合現(xiàn)象發(fā)生,本文采用微調(diào)的方法訓(xùn)練網(wǎng)絡(luò)。最后,通過(guò)多組實(shí)驗(yàn)的驗(yàn)證,與基于HOG特征的方法想比,該算法能夠明顯提升行人檢測(cè)的準(zhǔn)確率。(2)針對(duì)行人檢測(cè)系統(tǒng)中采用選擇性搜索算法(Selective Search,SEL)獲取預(yù)選區(qū)域效率低下的問(wèn)題,本文采用Edge Boxes算法優(yōu)化了行人檢測(cè)系統(tǒng)。預(yù)選窗口的獲取對(duì)于行人檢測(cè)系統(tǒng)至關(guān)重要,利用選擇性搜索算法提取一張圖像的預(yù)選區(qū)域需要花費(fèi)2秒左右,這嚴(yán)重影響了整個(gè)行人檢測(cè)系統(tǒng)的檢測(cè)效率。當(dāng)本文采用Edge Boxes算法提取預(yù)選區(qū)域時(shí),雖然檢測(cè)準(zhǔn)確率沒(méi)有明顯的提升,但只需要耗費(fèi)0.3秒的時(shí)間來(lái)提取一張圖片的窗口,大大改善了系統(tǒng)的檢測(cè)效率。(3)設(shè)計(jì)了基于快速區(qū)域卷積神經(jīng)網(wǎng)絡(luò)的行人檢測(cè)框架。針對(duì)采用深度卷積神經(jīng)網(wǎng)絡(luò)進(jìn)行特征提取難以保證實(shí)時(shí)性的問(wèn)題,本文在網(wǎng)絡(luò)模型中引入了感興趣區(qū)域匯聚層(RoI Pooling Layer),通過(guò)該層模型只需要對(duì)原圖像提取一次卷積特征,并將預(yù)選區(qū)域映射到特征圖(Feature Map)中后,得到固定維度的特征向量。實(shí)驗(yàn)表明,使用該方法在保證一定檢測(cè)準(zhǔn)確率的情況能夠極大的提升檢測(cè)速度,改善了算法的實(shí)時(shí)性和適用性。
[Abstract]:Pedestrian detection has always been a hot and difficult point in the field of machine vision. It has been widely used in intelligent monitoring, intelligent transportation and intelligent robot fields, such as traffic safety.Pedestrian detection technology can be used to pre-judge whether there are pedestrians in the front and nearby. If found, emergency braking can be taken immediately, which can effectively avoid vehicle collision with pedestrians and reduce casualties.Pedestrian detection is different from ordinary target detection. Pedestrians belong to non-rigid targets. In real life, pedestrians wear a variety of clothes, human posture varies, and the background is complex and changeable.Lack of light and mutual occlusion between pedestrians pose a great challenge to the work.Many effective pedestrian detection algorithms have been proposed, among which the most representative one is the gradient histogram of Oriented gradient histogram, but the detection effect is still not satisfactory in the more complex background.In recent years, deep learning has re-entered the perspective of people, among which the deep convolution neural network has made a great breakthrough in pattern recognition, which shows its superiority in feature extraction.In this paper, the pedestrian detection system based on regional convolution neural network is designed based on the research of pedestrian detection technology and depth learning, especially the deep convolution neural network model.Aiming at the high complexity of feature extraction in traditional artificial design and the difficulty of effectively expressing pedestrian features in complex scenes, this paper uses a deep convolution neural network model to detect pedestrians.The model combines lower level features to form more abstract high-level representation attribute classes or features, and then extracts more robust feature vectors from the samples.In order to prevent the over-fitting in training process, the network model is trained by fine-tuning method because of the deep level of the network model and the need for more training parameters, while the number of data samples labeled by manual pedestrian is less.Finally, through the verification of many experiments, compared with the method based on HOG feature, the algorithm can obviously improve the accuracy of pedestrian detection.In this paper, the Edge Boxes algorithm is used to optimize the pedestrian detection system.The acquisition of pre-selected window is very important for pedestrian detection system. It takes about 2 seconds to extract a pre-selected area of an image by selective search algorithm which seriously affects the detection efficiency of the whole pedestrian detection system.When the Edge Boxes algorithm is used to extract the pre-selected region, although the detection accuracy is not significantly improved, it only takes 0.3 seconds to extract a window of a picture.The detection efficiency of the system is greatly improved. A pedestrian detection framework based on fast area convolution neural network is designed.Aiming at the problem that it is difficult to guarantee the real-time performance of feature extraction by using deep convolution neural network, this paper introduces ROI Pooling layer into the network model, through which only one convolution feature is extracted from the original image.The feature vector of the fixed dimension is obtained by mapping the preselected region to the feature map.Experiments show that this method can greatly improve the detection speed and improve the real-time and applicability of the algorithm in the case of certain detection accuracy.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP183

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