The intelligent video surveillance system (IVSS) can automatically analyze the content of the surveillance image (SI) and reduce the burden of the manual labour. However, the SIs may suffer quality degradations in the procedure of acquisition, compression, and transmission, which makes IVSS hard to understand the content of SIs. In this paper, we first conduct an example experiment (i.e. the face detection task) to demonstrate that the quality of the SIs has a crucial impact on the performance of the IVSS, and then propose a saliency-based deep neural network for the blind quality assessment of the SIs, which helps IVSS to filter the low-quality SIs and improve the detection and recognition performance. Specifically, we first compute the saliency map of the SI to select the most salient local region since the salient regions usually contain rich semantic information for machine vision and thus have a great impact on the overall quality of the SIs. Next, the convolutional neural network (CNN) is adopted to extract quality-aware features for the whole image and local region, which are then mapped into the global and local quality scores through the fully connected (FC) network respectively. Finally, the overall quality score is computed as the weighted sum of the global and local quality scores. Experimental results on the SI quality database (SIQD) show that the proposed method outperforms all compared state-of-the-art BIQA methods.
翻译:智能视频监视系统(IVSS)可以自动分析监控图像的内容,并减轻人工劳动的负担;然而,在获取、压缩和传输过程中,SIS可能出现质量下降,使IVSS难以理解SI的内容。在本文中,我们首先进行一个实验(即面部检测任务),以表明SIs的质量对IVSS的性能有重大影响,然后为SIs盲目质量评估提出一个基于显著的深神经网络,帮助IVSS过滤低质量SI,改善检测和识别绩效。具体地说,我们首先绘制SIS的突出地图,以选择最突出的当地区域,因为突出区域通常含有丰富的机器视觉语义信息,从而对SIs的整体质量产生巨大影响。接着,采用革命神经网络(CNN)为整个图像和当地区域提取质量认知特征,然后通过SIQ质量全球质量评分系统(CFC)的拟议全球质量评分(BQQ-Q-Q-Q)通过完全连接的全球质量评分系统(SI)数据库,最终通过SI质量评分系统。