Abstract
Human detection in videos plays an important role in various real-life applications. Most traditional approaches depend on utilizing handcrafted features, which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods, which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for the human detection task. The pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with softmax and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high-performance Graphical Processing Unit (GPU).
Abstract (translated)
视频中的人体检测在各种现实应用中起着重要作用。大多数传统方法依赖于利用手工制作的特征，这些特征是问题相关的，并且对于特定任务是最优的。此外，它们对动态事件（如照明变化、摄像机抖动以及物体大小的变化）非常敏感。另一方面，所提出的特征学习方法成本较低且更易于实现，因为高度抽象和区分性的特征可以自动产生而无需专家知识。 在本文中，我们利用了自动特征学习的方法，这些方法结合了光流与三种不同的深度模型（即监督卷积神经网络(S-CNN)、预训练的CNN特征提取器以及分层极端学习机）来检测使用非静态摄像头从不同高度进行空中拍摄时视频中的行人。我们在公开且极具挑战性的UCF-ARG空中数据集上对这些模型进行了训练和测试，并分析了它们在训练准确性、测试准确性和学习速度方面的比较情况。性能评估考虑到了五种人类动作（挖掘、挥手、投掷、行走和跑步）。 实验结果显示，所提出的方法对于人体检测任务是成功的。预训练的CNN平均准确率为98.09%。S-CNN使用softmax时的平均准确率为95.6%，而使用支持向量机(SVM)时为91.7%。H-ELM的平均准确率为95.9%。在普通的中央处理器(CPU)上，H-ELM的训练时间需要445秒；而在高性能图形处理单元(GPU)上，S-CNN的学习时间为770秒。 这些结果表明，在视频中进行人体检测任务时，自动特征学习方法结合深度模型能够提供有效的解决方案，并且在效率和准确性方面表现出色。
URL
https://arxiv.org/abs/2601.00391
