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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Surpassing Humans and Computers with JellyBean: Crowd-Vision-Hybrid Counting Algorithms.

Akash Das Sarma¹, Ayush Jain², Arnab Nandi³

¹Stanford University.

Proceedings of the ... AAAI Conference on Human Computation and Crowdsourcing

|February 5, 2016

Summary

This summary is machine-generated.

The JellyBean algorithm efficiently counts objects in images by combining human crowdsourcing and computer vision. This novel approach improves accuracy and reduces costs for object counting tasks.

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Area of Science:

Computer Vision
Human-Computer Interaction
Image Processing

Background:

Object counting is crucial for various applications but faces challenges with traditional methods.
Supervised computer vision requires extensive labeled data and struggles with complex images.
Crowdsourcing alone can be inaccurate, especially for images with numerous objects.

Purpose of the Study:

To develop a cost-effective and accurate object counting algorithm.
To combine the strengths of crowdsourcing and computer vision for improved performance.
To introduce the JellyBean suite of algorithms for robust object counting.

Main Methods:

Utilizing a judicious decomposition of images to enhance counting accuracy.
Developing algorithms that are theoretically optimal or near-optimal in human query efficiency.
Implementing both stand-alone and hybrid modes for flexibility with computer vision integration.

Main Results:

Achieving high accuracy in object counting, even on challenging images.
Demonstrating cost-effectiveness compared to existing methods.
Validating the theoretical optimality and practical performance of the algorithms.

Conclusions:

The JellyBean suite offers a superior solution for object counting by integrating crowds and computer vision.
The hybrid approach provides flexibility and enhanced accuracy.
This method significantly outperforms individual workers or computer vision algorithms in complex scenarios.