Presentation yamin

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Paper presentation

iShadow: Design of a
wearable, real-time mobile
gaze tracker
Presenter: Yamin Tun
Addison Mayberry, Pan Hu, Benjamin
Marlin, Christopher Salthouse, Deepak
Ganesan (UMass)

Introduction

Gaze Tracking Criteria
 Efficient (power, memory)
 Fast
 Accurate

Methodology: Hardware
Eye-facing camera
World-facing camera

Methodology: Hardware
Architecture

Methodology: Hardware
challenges

Methodology: Prediction Model
Artificial Neural Network
(ANN):
Sparse sampling
 Learn best mask of a typical
eye image
Predict Gaze from
subsampled pixels

Methodology: Training ANN

Data Collection, Processing
 10 subjects
 ~3000 images at 10 Hz fps (~5 mins)

Key Results
~3 degree of error

Summary/Contribution
Low-power Real-time Gaze Tracker

Limitations/Critique
 Varying performance for different users
 Different depth of view for different faces
 Visibility of the entire eye
 Glasses placement
 Obstructing user’s view
 Imager + motion sensor
 Gaze on same object with head movement

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Bollworms are among the most damaging pests in cotton cultivation, affecting the bolls where the cotton fibers are formed. There are several species of bollworms, each capable of causing significant yield loss and quality degradation if not effectively managed. Here’s a detailed look at the primary bollworm species affecting cotton: Cotton Bollworm (Helicoverpa armigera): Also known as the corn earworm or the Old World bollworm, this pest is found in many regions around the world. It is highly polyphagous (feeds on many different plants) and poses a threat not only to cotton but also to maize, tomatoes, and legumes. The larvae bore into the cotton bolls, feeding on the developing seeds and fibers, which can lead to boll rot. Pink Bollworm (Pectinophora gossypiella): A significant pest of cotton, the pink bollworm larvae infest the cotton bolls, feeding on the seeds and lint. This can severely damage or destroy the bolls. In regions where pink bollworms are prevalent, they have been a major driver for the adoption of genetically engineered Bt cotton, which expresses a bacterium gene toxic to certain insects. Tobacco Budworm (Heliothis virescens): Closely related to the cotton bollworm, the tobacco budworm primarily attacks tobacco but is also a common pest in cotton. It primarily damages the flowers and bolls of the cotton plant. Differentiating between the tobacco budworm and the cotton bollworm based on appearance can be challenging, but it is crucial for effective management. American Bollworm (Helicoverpa zea): Known in some regions as the corn earworm, it is similar in behavior to Helicoverpa armigera and poses a threat to a variety of crops, including cotton. The larvae attack the cotton bolls, leading to direct damage to the cotton lint and seeds. Management Strategies: Cultural Controls: Crop rotation, destruction of crop residues, and deep plowing can help break the pest’s life cycle. Timing of planting can also be adjusted to avoid peak pest infestation. Biological Controls: Natural enemies like Trichogramma wasps, which parasitize bollworm eggs, and predators such as lacewings and ladybugs can be encouraged. Bacillus thuringiensis (Bt) products can also be sprayed, which are particularly effective against young larvae. Chemical Controls: Insecticides may be required when infestation levels exceed economic thresholds. However, resistance management must be considered, alternating modes of action to avoid developing resistance. Genetic Approaches: Bt cotton, genetically modified to express Bacillus thuringiensis toxin, has been highly effective in controlling bollworms and has dramatically reduced the reliance on chemical insecticides. Monitoring and Scouting: Regular field scouting and using pheromone traps to monitor adult populations can help in timely and targeted application of control measures. The effective management of bollworms often requires an integrated approach

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Presentation yamin

1. iShadow: Design of a wearable, real-time mobile gaze tracker Presenter: Yamin Tun Addison Mayberry, Pan Hu, Benjamin Marlin, Christopher Salthouse, Deepak Ganesan (UMass)

2. Introduction

3. Gaze Tracking Criteria  Efficient (power, memory)  Fast  Accurate

4. Methodology: Hardware Eye-facing camera World-facing camera

5. Methodology: Hardware Architecture

6. Methodology: Hardware challenges

7. Methodology: Prediction Model Artificial Neural Network (ANN): Sparse sampling  Learn best mask of a typical eye image Predict Gaze from subsampled pixels

8. Methodology: Training ANN

9. Data Collection, Processing  10 subjects  ~3000 images at 10 Hz fps (~5 mins)

10. Key Results ~3 degree of error

11. Summary/Contribution Low-power Real-time Gaze Tracker

12. Limitations/Critique  Varying performance for different users  Different depth of view for different faces  Visibility of the entire eye  Glasses placement  Obstructing user’s view  Imager + motion sensor  Gaze on same object with head movement

Editor's Notes

What is Gaze Tracking? Gaze Tracking is measuring gaze points- estimating where a person is looking at in the physical world using eye movement information Applications- Continuous real-time gaze tracking can be valuable/useful in a lot of applications such as detection of unsafe behavior- such as tracking driver’s gaze and detect whether driver has attention on the road- prevent accidents- pedestrian and cars Detecting psychological behaviors- Pupil dilation- excitement Pupil micromovements-detect fatigue Power hungry, computation intensive device -&gt; bulky wearable devices
Easy Comp Vision problem- detect circle or the darkest patches- probably using some powerful MCU -real-time -power - Google glasses- power consuming, as a wearable device- not realistic to run out of battery all the time - but to do it in real-time with limited computation power- challenge
-right now, glasses use eye-facing camera at the back
STM32 Arm cortex- MCU Stonyman- grayscale imager Storage
How the eyeglasses work? -feed in Eye images from back camera-&gt; Output=gaze estimate -Neural network to estimate gaze -&gt;to build it, offline-training ~3 degree of error on average= gaze estimate error