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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2018-12, Vol.18 (12), p.4385
• Main Authors: Martín-Nieto, Rafael, García-Martín, Álvaro, Martínez, José M, SanMiguel, Juan C
• Format: Article
• Language: English
• Subjects: automatic parametrization; multi-camera; people detection; self-correlation maximization
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18124385

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.