Search and rescue are essential applications of disaster management in which people are evacuated from the disaster-prone area to a safer place. This overall process of search and rescue can be more efficient if an automated system can quickly locate the human or area where rescue is required. To provide a faster and accurate search of those places, this paper proposes a novel approach to search and rescue using automated drone surveillance. In this paper, a complex scene classification problem is solved using the proposed 3DCNN model. The proposed model uses spatial as well as temporal features of the video for the classification of the scene as help or non-help in the natural disaster. Due to the unavailability of such kind of dataset, it is impossible to train the model. Therefore, it is essential to develop a dataset for search and rescue. The proposed dataset is a first and unique dataset for scene classification using drone surveillance. The major contribution of this paper is (1) a novel 3DCNN powered model for scene classification in drone surveillance, (2) to develop the required dataset for the training of scene classification model, and (3) particular swarm optimization (PSO)-based hyper-parameter tuning for getting the best value of multiple parameters used for training the model. Our hybridization of parameter tuning with PSO helps for the convergence of parameter values of proposed 3DCNN model, and the proposed scene classification model (3DCNN+PSO) is applied to the dataset. The proposed model gives an impressive performance to help situation identification with 98% training and 99% validation accuracy.

搜索和救援是灾难管理的重要应用程序，其中人们被从灾难多发地区疏散到一个更安全的地方。如果自动化系统可以快速定位需要救援的人员或区域，那么整个搜索和救援过程将更加高效。为了提供对这些地点的更快，准确的搜索，本文提出了一种使用自动无人机监视进行搜索和救援的新颖方法。在本文中，使用提出的3DCNN模型解决了复杂的场景分类问题。提出的模型使用视频的空间和时间特征将场景分类为自然灾害中的帮助或非帮助。由于此类数据集不可用，因此无法训练模型。因此，开发用于搜救的数据集至关重要。提议的数据集是使用无人机监视进行场景分类的第一个唯一数据集。本文的主要贡献是（1）新型3DCNN动力模型用于无人机监视中的场景分类；（2）开发用于训练场景分类模型所需的数据集；以及（3）基于特定群体优化（PSO）的模型超参数调整，以获取用于训练模型的多个参数的最佳值。我们的参数调整与PSO的混合有助于收敛所建议的3DCNN模型的参数值，并将所建议的场景分类模型（3DCNN + PSO）应用于数据集。提出的模型以98％的训练和99％的验证准确度提供了令人印象深刻的性能来帮助识别情况。