Convolutional Neural Networks (CNN) have provided new and accurate methods for processing digital images and videos. Yet, training CNNs is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed. Furthermore, the final systems tend to operate as black boxes that are difficult to interpret. The current thesis considers the problem of detecting faces from the AOLME video dataset. The AOLME dataset consists of a large video collection of group interactions that are recorded in unconstrained classroom environments. For the thesis, still image frames were extracted at every minute from 18 24-minute videos. Then, each video frame was divided into 9x5 blocks with 50x50 pixels each. For each of the 19440 blocks, the percentage of face pixels was set as ground truth. Face detection was then defined as a regression problem for determining the face pixel percentage for each block. For testing different methods, 12 videos were used for training and validation. The remaining 6 videos were used for testing. The thesis examines the impact of using the instantaneous phase for the AOLME block-based face detection application. For comparison, the thesis compares the use of the Frequency Modulation image based on the instantaneous phase, the use of the instantaneous amplitude, and the original gray scale image. To generate the FM and AM inputs, the thesis uses dominant component analysis that aims to decrease the training overhead while maintaining interpretability.
翻译:连锁神经网络(CNN)为处理数字图像和视频提供了新的和准确的方法。然而,培训CNN在计算资源方面要求极高。此外,对于具体的应用程序,传输学习的标准使用也往往需要比可能需要的资源多得多。此外,最后系统往往作为难以解释的黑盒运作。当前的论文认为从AOLME视频数据集中探测面部的问题。AOLME数据集包含大量视频集成的团体互动,这些互动记录在不受限制的课堂环境中。对于论文而言,从18个24分钟的视频中每分钟提取一次图像框。然后,每个视频框被分为9x5个区块,每个区50x50像素。对于19440个区中的每一块,脸部象素的百分比被设定为难以解释的黑盒。然后,面对面检测被定义为确定每个区面像素百分比的回归问题。为了测试不同的方法,使用了12个视频来进行培训和校验。其余的6个视频用于测试。对于利用瞬时的图像分析,利用瞬间分析阶段的磁带分析,对磁带的图像进行了分析,同时使用磁带分析,对磁带分析,对磁段进行磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁带分析,对磁段分析,对磁到磁段分析,对磁段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段段进行。