Zahra Asma, Ghafoor Mubeen, Munir Kamran, Ullah Ata, Ul Abideen Zain
Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
Department of Computer Science, National University of Modern Languages, Islamabad, Pakistan.
Multimed Tools Appl. 2021 Dec 27:1-26. doi: 10.1007/s11042-021-11468-w.
Smart video surveillance helps to build more robust smart city environment. The varied angle cameras act as smart sensors and collect visual data from smart city environment and transmit it for further visual analysis. The transmitted visual data is required to be in high quality for efficient analysis which is a challenging task while transmitting videos on low capacity bandwidth communication channels. In latest smart surveillance cameras, high quality of video transmission is maintained through various video encoding techniques such as high efficiency video coding. However, these video coding techniques still provide limited capabilities and the demand of high-quality based encoding for salient regions such as pedestrians, vehicles, cyclist/motorcyclist and road in video surveillance systems is still not met. This work is a contribution towards building an efficient salient region-based surveillance framework for smart cities. The proposed framework integrates a deep learning-based video surveillance technique that extracts salient regions from a video frame without information loss, and then encodes it in reduced size. We have applied this approach in diverse case studies environments of smart city to test the applicability of the framework. The successful result in terms of bitrate 56.92%, peak signal to noise ratio 5.35 bd and SR based segmentation accuracy of 92% and 96% for two different benchmark datasets is the outcome of proposed work. Consequently, the generation of less computational region-based video data makes it adaptable to improve surveillance solution in Smart Cities.
智能视频监控有助于构建更强大的智慧城市环境。多角度摄像头充当智能传感器，从智慧城市环境中收集视觉数据并将其传输以进行进一步的视觉分析。为了进行高效分析，传输的视觉数据需要高质量，而在低容量带宽通信信道上传输视频时，这是一项具有挑战性的任务。在最新的智能监控摄像头中，通过各种视频编码技术（如高效视频编码）来保持高质量的视频传输。然而，这些视频编码技术仍然提供有限的功能，视频监控系统中对行人、车辆、骑自行车者/骑摩托车者和道路等显著区域基于高质量的编码需求仍未得到满足。这项工作为构建一个高效的基于显著区域的智慧城市监控框架做出了贡献。所提出的框架集成了一种基于深度学习的视频监控技术，该技术可以从视频帧中提取显著区域而不损失信息，然后以减小的尺寸对其进行编码。我们已将此方法应用于智慧城市的各种案例研究环境中，以测试该框架的适用性。对于两个不同的基准数据集，在比特率方面取得了56.92%、峰值信噪比方面取得了5.35 bd以及基于结构相似性（SR）的分割精度分别为92%和96%的成功结果是所提出工作的成果。因此，生成基于计算量更少的区域的视频数据使其适用于改进智慧城市中的监控解决方案。
