Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization †

Martín-Nieto, Rafael; García-Martín, Álvaro; Martínez, José M.; SanMiguel, Juan C.

doi:10.3390/s18124385

Open AccessArticle

Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization ^†

by

Rafael Martín-Nieto

^‡

,

Álvaro García-Martín

^*,‡

,

José M. Martínez

^‡

and

Juan C. SanMiguel

^‡

Video Processing and Understanding Laboratory (VPULab), Universidad Autónoma de Madrid, 28049 Madrid, Spain

^*

Author to whom correspondence should be addressed.

^†

This paper is an extended version of our paper published in García-Martín, A.; SanMiguel, J.C. Adaptive people detection based on cross-correlation maximization. In Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 17–20 September 2017.

^‡

These authors contributed equally to this work.

Sensors 2018, 18(12), 4385; https://doi.org/10.3390/s18124385

Submission received: 5 November 2018 / Revised: 7 December 2018 / Accepted: 8 December 2018 / Published: 11 December 2018

(This article belongs to the Section Intelligent Sensors)

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Abstract

Finding optimal parametrizations for people detectors is a complicated task due to the large number of parameters and the high variability of application scenarios. In this paper, we propose a framework to adapt and improve any detector automatically in multi-camera scenarios where people are observed from various viewpoints. By accurately transferring detector results between camera viewpoints and by self-correlating these transferred results, the best configuration (in this paper, the detection threshold) for each detector-viewpoint pair is identified online without requiring any additional manually-labeled ground truth apart from the offline training of the detection model. Such a configuration consists of establishing the confidence detection threshold present in every people detector, which is a critical parameter affecting detection performance. The experimental results demonstrate that the proposed framework improves the performance of four different state-of-the-art detectors (DPM , ACF, faster R-CNN, and YOLO9000) whose Optimal Fixed Thresholds (OFTs) have been determined and fixed during training time using standard datasets.

Keywords: self-correlation maximization; multi-camera; people detection; automatic parametrization

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MDPI and ACS Style

Martín-Nieto, R.; García-Martín, Á.; Martínez, J.M.; SanMiguel, J.C. Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization ^†. Sensors 2018, 18, 4385. https://doi.org/10.3390/s18124385

AMA Style

Martín-Nieto R, García-Martín Á, Martínez JM, SanMiguel JC. Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization ^†. Sensors. 2018; 18(12):4385. https://doi.org/10.3390/s18124385

Chicago/Turabian Style

Martín-Nieto, Rafael, Álvaro García-Martín, José M. Martínez, and Juan C. SanMiguel. 2018. "Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization ^†" Sensors 18, no. 12: 4385. https://doi.org/10.3390/s18124385

APA Style

Martín-Nieto, R., García-Martín, Á., Martínez, J. M., & SanMiguel, J. C. (2018). Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization ^†. Sensors, 18(12), 4385. https://doi.org/10.3390/s18124385

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Enhancing Multi-Camera People Detection by Online Automatic Parametrization Using Detection Transfer and Self-Correlation Maximization ^†

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