E-commerce, Website

1. E-COMMERCE WEBSITE USING ANGULAR
Authors:
Vishwakarma Institute of Technology, Pune
WT COURSE PROJECT : TY 2022 - 2023
Department of E&TC Engineering
Name Roll No.
Ajinkya Rajaram Edhate 65
Shubham Prakashrao Gawande 74
Atharva Shankarrao Hambarde 77

2. Index
Introduction
Research Gap
Novelty
Methodology
Results and Analysis
Conclusion
References

3. Introduction
• Entire fate of any game rests with the umpire, but wrong umpiring
can spoil the game.
• To avoid Human Perception, No ball detection is useful.
• This model gives probability of an image being a no ball or legal
ball/
• previous research regarding this technology to solve the problem,
but these are infeasible due to usage of sensors on the field and
bowlers.

4. Hardware Requirement
Laptop
• Html
• CSS
• PHP
• JavaScript
• Database
SOFTWARE/TOOLS REQUIREMENT
PROPOSED IMPLEMENTATION LANGUAGE(S)
• Sublime Text
• Xammp Server

5. Literature Review

6. Application of Computer Vision in
Cricket: Foot Overstep No-Ball Detection
In this paper, crease is divided the bowling
crease into two regions and applied image
subtraction method on both regions to find the
change in pixel values. Later, we have applied
our proposed method on real world video frames
Literature Survey

7. No Ball
Fig. 2 shows the foot of bowler in action
while delivering the ball. If the shoe of
bowler is in state 1 i.e., the foot is in No
Ball region. In state 2, some part of shoe
of bowler is in Legal Region, so it’s a
Legal Ball.

8. Research Gap
Use of automatic ball Detection
instead of using on field sensors
Less Expensive

9. Novelty
Inexpensive
Directly predicts image as No ball or Legal
ball based on probability

10. Dataset
• Input dataset contains 100 images.
• Dataset is divided in two parts No Ball and legal Ball.
• We have used 80% of dataset for training and 20% of
dataset for testing.

11. Methodology
 In order to automatically detect and distinguish
foot overstepping no balls from fair balls,
implemented a Convolution Neural Network
(CNN) based classification method with
VGG19
 Transfer learning algorithms, which take the
knowledge gained from solving one problem
and apply it to another are used.

12. Algorithm

13. Results

14. Conclusion
Computationally efficient
No Human intervention
Highest accuracy achieved is 82.7%
Transfer Learning algorithm is used
Suitable for real-time applications

15. References
1. “Law 36 (Leg before wicket),” Lords.org, 2016. [Online]. Available: https://www.lords.org/mcc/laws-of-cricket/laws/law-36-leg-before-
wicket/.
2. “Law 24 (No ball),” Lords.org, 2016. [Online]. Available: https://www.lords.org/mcc/laws-of-cricket/laws/law-24-no-ball/
3. D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, vol. 60, no. 2, pp. 91-
110, 2004.
4. T. Kadir, P. Hobson, M. Brady, and J. Close, “FROM SALIENT FEATURE TO SCENE DESCRIPTION,” Workshop on Image Analysis
for Multimedia Interactive Services. 2005, pp. 2–5
5. S. Baker and I. Matthews, “Lucas-Kanade 20 Years On: A Unifying Framework,” International Journal of Computer Vision, vol. 56, no. 3,
pp. 221-255, 2004.
6. D.a. Forsyth and V.O. Brien, “Computer Vision second edition,” Computer Vision: A Modern Approach (2003): 88-101.
7. RukshanPramoditha,https://towardsdatascience.com/coding-a-convolutional-neural-network-cnn-using-keras-sequential-api-ec5211126875
8. AZM Ehtesham Chowdhury, Md Shamsur Rahim, Md Asif Ur Rahman, “Application of Computer Vision in Cricket: Foot Overstep No-
Ball Detection”, Department of Computer Science American International University-Bangladesh Dhaka, Bangladesh
•

16. Thank You!

17. Any Questions?