Motion and geometry-related information fusion through a framework for object identification from a moving camera in urban driving scenarios

Lateef, Fahad; Kas, Mohamed; Ruichek, Yassine

doi:10.1016/j.trc.2023.104271

Cited by 1 publication

(3 citation statements)

References 30 publications

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“…This method represents a breakthrough in the quantitative analysis of microscopy videos, offering an efficient and automated solution for the quantification of cellular and subcellular dynamics without the labor-intensive manual annotation. Fahad Lateef et al [57] propose an innovative object identification framework (FOI) for autonomous vehicles, focusing exclusively on camera data to detect and analyze objects in urban driving scenarios. This framework uses image registration algorithms and optical flow estimation to compensate for self-motion and extract accurate motion information from moving objects from a mobile camera.…”

Section: Improvements In Annotation and Efficiencymentioning

confidence: 99%

“…Lateef et al [57] Presents a framework for object identification in autonomous vehicles using cameras. It uses image registration and optical flow for motion analysis.…”

Section: Fahadmentioning

confidence: 99%

“…These techniques, which transform images into more malleable representations such as point clouds, highlight the potential of deep learning to extract and analyze information in an efficient and innovative way. The challenge of detecting and analyzing movements in dynamic scenarios is addressed by Fahad Lateef et al [57] and Roberto Henschel et al [50], who apply object tracking to contexts of urban mobility and human interactions, respectively. These studies illustrate how technology can be adapted to improve public safety and understand complex behavior in crowded environments.…”

Section: A Object Trackingmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Image Annotation With Object Tracking and Image Retrieval: A Systematic Review

Fernandes,

Pessoa,

Salgado

et al. 2024

IEEE Access

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Effective image and video annotation is a fundamental pillar in computer vision and artificial intelligence, crucial for the development of accurate machine learning models. Object tracking and image retrieval techniques are essential in this process, significantly improving the efficiency and accuracy of automatic annotation. This paper systematically investigates object tracking and image acquisition techniques. It explores how these technologies can collectively enhance the efficiency and accuracy of the annotation processes for image and video datasets. Object tracking is examined for its role in automating annotations by tracking objects across video sequences, while image retrieval is evaluated for its ability to suggest annotations for new images based on existing data. The review encompasses diverse methodologies, including advanced neural networks and machine learning techniques, highlighting their effectiveness in various contexts like medical analyses and urban monitoring. Despite notable advancements, challenges such as algorithm robustness and effective human-AI collaboration are identified. This review provides valuable insights into these technologies' current state and future potential in improving image annotation processes, even showing existing applications of these techniques and their full potential when combined.

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Section: Improvements In Annotation and Efficiencymentioning

confidence: 99%

“…Lateef et al [57] Presents a framework for object identification in autonomous vehicles using cameras. It uses image registration and optical flow for motion analysis.…”

Section: Fahadmentioning

confidence: 99%