Abstract

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 efcient 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 efciency 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 efcient salient region-based sur�veillance 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 diferent 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.