Crowd emotion understanding is an interesting research area that assists the security personnel to read the emotion/activity of the crowd in the locality. Most of the traditional methods utilize the low-level visual features to understand the crowd emotions that extend the gap between the low- and the high-level features. With the aim to develop an automatic method for emotion recognition, this paper utilizes the deep convolutional neural network (deep CNN). For the effective emotion recognition, it is essential to select the key frames of the video using the wavelet-based Bhattacharya distance. The key frames are fed to the space-time interest points descriptor that extracts the features and forms the input vector to the classifier. Deep CNN is trained using the proposed Stochastic Gradient Descent–Whale Optimization Algorithm, which is the unification of the standard stochastic gradient descent algorithm with whale optimization algorithm. The proposed classifier recognizes the emotions of the crowd, such as angry, escape, fight, happy, normal, running/walking and violence. The analysis of the method proved that the proposed approaches acquired a maximal accuracy, specificity and sensitivity of 0.9693, 0.9936 and 0.9675, respectively.

中文翻译：

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基于随机梯度下降-鲸鱼优化算法的深度卷积神经网络对人群情感理解

人群情感理解是一个有趣的研究领域，可帮助安全人员阅读当地人群的情感/活动。大多数传统方法都利用低级视觉功能来了解人群情绪，从而扩大了低级和高级功能之间的差距。为了开发一种自动的情感识别方法，本文利用了深度卷积神经网络（deep CNN）。为了有效地进行情感识别，必须使用基于小波的Bhattacharya距离选择视频的关键帧。关键帧被馈送到时空兴趣点描述符，该描述符提取特征并形成分类器的输入向量。使用建议的随机梯度下降-鲸鱼优化算法对深层CNN进行了训练，这是标准随机梯度下降算法与鲸鱼优化算法的统一。拟议的分类器可以识别人群的情绪，例如生气，逃避，战斗，快乐，正常，奔跑/行走和暴力。该方法的分析证明，所提出的方法分别获得了0.9693、0.9936和0.9675的最大准确性，特异性和敏感性。