Objects and Distractions Segmentation Dataset

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Project Overview:

Objective

Objects and Distractions Segmentation Dataset: Create a dataset specialized in distinguishing primary objects of interest from background distractions in various scenarios. This dataset is vital for enhancing technologies like focus-based photography, attention-driven UI/UX, and safety applications where primary object recognition is crucial.

Scope

In this curated collection of images, each encapsulates commonplace scenarios wherein an object of interest is intricately juxtaposed with potential distractions. Seamlessly transitioning from one scenario to another, these images adeptly capture diverse contexts where focal points intricately contend with surrounding elements. Spanning bustling city streets to serene natural landscapes, the juxtaposition of main objects and background distractions intricately manifests itself. Each image undergoes meticulous pixel-wise annotation, meticulously delineating both the prominent objects and the myriad of distractions that vie for attention within the frame.

Sources

Meticulously collected crowdsourced photos from social media platforms under appropriate permissions have been successfully curated for a comprehensive dataset. Additionally, established partnerships with photography schools and professionals have contributed to a carefully collected and thoughtfully curated assortment of visual content. Furthermore, capture sessions were conducted in environments known for visual distractions, such as bustling marketplaces, active playgrounds, and traffic junctions, resulting in a successfully collected and professionally curated set of images. Moreover, engagements in collaborations with app developers and UX designers for interface-based distractions have contributed to a successfully collected and curated dataset tailored for usability studies.

Data Collection Metrics

Total Images: 30,000
Natural Environments: 10,000
Urban Settings: 8,000
Indoor Situations: 7,000
Digital Interfaces: 5,000

Annotation Process

Stages

Image Pre-processing: Firstly, adjusting images for uniformity in lighting, clarity, and resolution to maintain dataset consistency is crucial. Subsequently,
pixel-wise segmentation will be conducted, wherein annotators will use specialized software to demarcate primary objects from background distractions in each image.
Validation: A secondary review will be done on each annotation to ascertain precision and alignment with project objectives.

Annotation Metrics

In total, there are 30,000 pixel-wise annotations, with one for each image.
On average, it takes 20 minutes to annotate each image, considering the complexity of differentiating objects from distractions.”)

Quality Assurance

Stages

Automated Model Assessment: Early-stage segmentation algorithms, therefore, assist in highlighting potential discrepancies in annotations. Additionally, Peer Review: a subset of annotations undergoes peer review to maintain and bolster quality standards. Furthermore, Inter-annotator Agreement: certain images are annotated by multiple personnel to achieve a consensus on what defines a distraction.

QA Metrics

Annotation Validation Cases: 1,500 (representing 10% of the total), are essential for ensuring accuracy in our data. Additionally, as part of our data cleansing process, we meticulously remove poor-quality or irrelevant images.

Conclusion

The Objects and Distractions Segmentation Dataset serves as a pivotal bridge in attention-focused applications. By precisely delineating between primary objects and secondary distractions, this dataset holds the potential to propel innovations in various domains. Specifically, it promises to revolutionize photography, digital interfaces, and safety systems. By enabling machines to discern and prioritize elements much like humans do inherently, this dataset stands as a cornerstone in advancing AI capabilities.

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