Image resolution impact analysis on pedestrian detection in smart cities surveillance

Dinakaran, Ranjith and Sexton, Graham and Seker, Huseyin and Bouridane, Ahmed and Jiang, Richard (2017) Image resolution impact analysis on pedestrian detection in smart cities surveillance. In: Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017. ACM International Conference Proceeding Series . Association for Computing Machinery (ACM), GBR. ISBN 9781450352437

Full text not available from this repository.

Abstract

In the paradigm of smart cities 1 , video surveillance is becoming a widely-applied technology to help improve the quality of human life in the era of digital living, where pedestrian detection is one of the key components for people-centered smart cities applications including wellbeing, security, traffic guiding, and unmanned vehicles, etc. While so far most surveillance cameras are of low quality resolution for cost-saving reasons, the impact of the image resolution on detection accuracy becomes a concerned issue. Though lower resolution can save the cost as well as the processing time, it has not been clearly reported how the resolution can impact on the detection accuracy. In this paper, we investigate the limit of low-resolution cameras with regards to the accuracy of the pedestrian detection, and experimentally demonstrate its impact on the most widely-applied HOG-SVM pedestrian detector, which is a combination of Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for pedestrian detection. From our experiments, it is found that there is an optimal resolution to balance between speed and accuracy, while we show in our experiments that the resolution has apparent influence on both the accuracy and the computing time.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
ID Code:
134349
Deposited By:
Deposited On:
01 Aug 2019 14:15
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Sep 2020 04:24