Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 17/10/2017 |
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Host publication | Proceedings of the International Conference on Internet of Things and Machine Learning, IML 2017 |
Editors | Hani Hamdan, Faouzi Hidoussi, Djallel Eddine Boubiche |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Number of pages | 8 |
ISBN (electronic) | 9781450352437 |
<mark>Original language</mark> | English |
Event | 1st International Conference on Internet of Things and Machine Learning, IML 2017 - Liverpool, United Kingdom Duration: 17/10/2017 → 18/10/2017 |
Conference | 1st International Conference on Internet of Things and Machine Learning, IML 2017 |
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Country/Territory | United Kingdom |
City | Liverpool |
Period | 17/10/17 → 18/10/17 |
Name | ACM International Conference Proceeding Series |
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Conference | 1st International Conference on Internet of Things and Machine Learning, IML 2017 |
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Country/Territory | United Kingdom |
City | Liverpool |
Period | 17/10/17 → 18/10/17 |
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.