VizWiz::LocateIt - enabling blind people to locate objects in their environment

Bigham, Jeffrey P.; Jayant, Chandrika; Miller, Andrew; White, Brandyn; Yeh, Tom

doi:10.1109/cvprw.2010.5543821

Cited by 102 publications

(56 citation statements)

References 15 publications

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“…Whether performing real-time optical character recognition to help the blind [1], aggregating and mapping microblog data for disaster relief [2], or labeling and classifying images for ornithology research [3], people can be used to perform signal processing and information processing tasks that are difficult for machines. Although the use of human computation in larger information systems has a long history [4], [5], enabled by modern communication technologies the use of such crowd systems has flourished of late and now forms the basis for many enterprises [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Participation in crowd systems

Varshney

2012

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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There is growing interest in using crowds of human workers to perform information processing tasks that are difficult for machines. At IBM, we have deployed a large internal crowdsourcing contest system for software development, which has now successfully completed thousands of tasks. Contrary to concerns of power efficiency and bandwidth in machine systems, player participation and motivation are key concerns in such crowd-based human computation systems. In this paper, we demonstrate that player participation is welldescribed by a power-law distribution, much like Lotka's Law of scientific productivity. We further describe a momentumbased generative model for participation that explains the observed empirical phenomenon and provide a thermodynamic interpretation, discussing what it means for a crowd system to heat up. Overall, player motivation is essential for driving participation and ensuring a reliable delivery platform; several intrinsic, extrinsic, and social motivators have been identified.

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Section: Introductionmentioning

confidence: 99%

Participation in crowd systems

Varshney

2012

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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“…Most closely related to our work are the systems by Hub et al [8], Caperna et al [3], and Bigham et al [2]. In 2004, Hub et al [8] presented a system that assists blind users in orienting themselves in indoor environments.…”

Section: Related Workmentioning

confidence: 81%

“…However, the corresponding evaluation has been performed in a simplified scenario and computer vision was left as major aspect for future work. Bigham et al [2] use Speeded Up Robust Features (SURF; see [10]) for object identification, but instead of training an object database (see, e.g., [3]), they send images with user requests (e.g., where is the object in the image) to Amazon's Mechanical Turk [1] where humans can outline the objects. The outlines of the object can then be used to estimate the object's location in the environment and guide the user towards the object by informing the user how close he is to the target [2].…”

Section: Related Workmentioning

confidence: 99%

An Assistive Vision System for the Blind That Helps Find Lost Things

Schauerte

Martínez

Constantinescu

et al. 2012

Lecture Notes in Computer Science

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Abstract. We present a computer vision system that helps blind people find lost objects. To this end, we combine color-and SIFT-based object detection with sonification to guide the hand of the user towards potential target object locations. This way, we are able to guide the user's attention and effectively reduce the space in the environment that needs to be explored. We verified the suitability of the proposed system in a user study.

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“…Provision of this information is feasible by using the technique of computer vision. As mentioned in Vázquez and Steinfeld (2012), some mobile applications have been developed to provide information of size, number and position of photographed objects and even direct the visually impaired to focalise interested objects for taking pictures (e.g., Bigham et al 2010;Jayant et al 2011). By implementing further information, we can expect that the vibration function of mobile devices could help them to perceive simple graphic information.…”

Section: Discussionmentioning

confidence: 99%