3D pedestrian localization using multiple cameras: a generalizable approach

Lima, João Paulo; Roberto, Rafael; Figueiredo, Lucas; Simões, Francisco; Thomas, Diego; Uchiyama, Hideaki; Teichrieb, Verônica

doi:10.1007/s00138-022-01323-9

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…This single-step approach leads to better results but realizing it requires the availability of a dataset with ground plane positions of the pedestrians. By first performing object detection and projecting these 2D detections to ground plane positions [7,23], only bounding box annotations on 2D camera frames are required. Such labels are much easier to obtain due to the readily available tooling [24,25].…”

Section: Related Workmentioning

confidence: 99%

Multi-camera detection framework for lifelong broiler flock monitoring

Cardoen,

Leroux,

Simoens

2024

Preprint

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In recent times, there has been an increased emphasis on chicken welfare within the poultry industry, leading to the adoption of camera-based solutions for continuous monitoring of broilers' activity and health parameters. Accurate detection and localization of broilers within the pen are crucial input for various welfare monitoring applications. However, current research has predominantly concentrated on single-camera setups, which are prone to occlusions caused by feeder or lighting equipment and struggle with scalability in larger pen sizes. To overcome these challenges, we propose a multi-camera setup that allows precise detection and localization of each broiler in a top-down view of the pen. Our approach ensures detection accuracy throughout the entire six-week lifespan of broilers, providing robust and consistent results despite the significant changes in the animals' visual appearance over time. We obtain a MODA score of 83.8% on real-world data and demonstrate its superior effectiveness compared to single-camera setups.

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Section: Related Workmentioning

confidence: 99%

Multi-camera detection framework for lifelong broiler flock monitoring

Cardoen,

Leroux,

Simoens

2024

Preprint

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“…Usually, this is performed in person by observing the region and registering the usage by residents. Researchers have been proposing the usage of pedestrian tracking [85] to understand this relation and plan interventions. This scenario could be improved by understanding emotion.…”

Section: Application Scenariosmentioning

confidence: 99%

A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability

et al. 2023

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Emotion recognition is the task of identifying and understanding human emotions from data. In the field of computer vision, there is a growing interest due to the wide range of possible applications in smart cities, health, marketing, and surveillance, among others. To date, several datasets have been proposed to allow techniques to be trained, validated, and finally deployed to production. However, these techniques have several limitations related to the construction of these datasets. In this work, we survey the datasets currently employed in state-of-the-art emotion recognition, to list and discuss their applicability and limitations in real-world scenarios. We propose experiments on the data to extract essential insights related to the provided visual information in each dataset and discuss how they impact the training and validation of techniques. We also investigate the presence of nonverbal cues in the datasets and propose experiments regarding their representativeness, visibility, and data quality. Among other discussions, we show that EMOTIC has more diverse context representations than CAER, however, with conflicting annotations. Finally, we discuss application scenarios and how techniques to approach them could leverage these datasets, suggesting approaches based on findings from these datasets to help guide future research and deployment. With this work we expect to provide a roadmap for upcoming research and experimentation in emotion recognition under real-world conditions.

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