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Definition of Reputation Credits
Mina Abd El-Massih
Cairo, Egypt Gmail: [email protected] Phone: +201284400156
Education
2015 - 2019
Ain Shams University
Scientific Computing Department
Ranked 2nd out of 135, GPA: Very Good.
Graduation Project Grade: Excellent.
Work Experience
January 2020 - August 2022
Machine Learning | Computer Vision Engineer
Advanced Programs Trd Co.
- Working on Video Analytics projects to turn Riyadh city into a smart city.
- Training and finetuning SOTA models like YoloV4, Faster RCNN, etc..
- Working on challenging real-life projects using state-of-the art trackers.
- Dataset creating, cleaning, ..etc
- Hands-on experience with Deepstream | Nvidia TLT.
- Hands on experience with using, modifying Gstreamer pipelines in Python.
March 2019 - May 2019
Research & Development Internship
Valeo
-I was a research and development intern at Valeo, I worked on autonomous driving and participated in CARLA challenge representing Valeo in the challenge.
-The challenge was mainly about using deep learning to allow a simulated car to pass a couple of scenarios which show how good the car is at autonomous driving and how good the car passes the scenarios.
Projects
Accident Detection
Using information obtained from tracker, I was able to detect anomalies by calculating pixel velocities and direction of vehicles moving and from that information a normal range of velocities and magnitudes was generated and any outliers were considered anomalies which cause or had accidents.
Vehicle Entering Restricted Area
Implemented with Python and OpenCV, using YoloV4 and Nvidia's Deepstream 5 NvDCF tracker.
Object Dropped/Removed detection
Implemented with Python, using background subtraction SubSENSE algorithm.
Vehicle Color/Type Classification Analytics
Creating analytics based on vehicle colors and/or type, for example, a video would be created of all the red cars that pass along a certain street. The analytics are extracted from a given long video of the camera's feed or a live feed from cameras, implemented using Yolo with a tracker on Deep Stream.
Violence Recognition
The graduation Project, implemented with Python, Keras and OpenCV, a deep learning model was created in which a pretrained VGG-16 model is followed by an LSTM to extract temporal features and then it was trained on those extracted features to decide if violence existed or not. A dataset was created and added on Kaggle, can be found here.
I presented the publication about it in the presence of leading researchers and scientists. Publication can be found here.
Publications
December 2019
Violence Recognition from videos using Deep Learning Techniques ICICIS 9th IEEE Conference
Courses
1. Andrew NG's Deep Learning Specialization
2. Udacity's Computer Vision Nanodegree
AI Technical Skills
Soft Skills
Languages
- Python
- Anaconda | Pip | Docker
- ML | DL | CNN
- Keras | Onnx | MXNet | Gluon
- Computer Vision
- Github | git
- Linux
- Deepstream5
- Gstreamer (Python)
- Google Cloud Platform
- Teamwork.
- Public speaking.
- Problem Solver.
- Self Learner.
- Fast Learner.
- Ability to work alone.
- Self-motivated.
- Arabic - Native
- English - Professional
Mina Abd El-Massih
Cairo, Egypt Gmail: [email protected] Phone: +201284400156
Education
2015 - 2019
Ain Shams University
Scientific Computing Department
Ranked 2nd out of 135, GPA: Very Good.
Graduation Project Grade: Excellent.
Work Experience
January 2020 - August 2022
Machine Learning | Computer Vision Engineer
Advanced Programs Trd Co.
- Working on Video Analytics projects to turn Riyadh city into a smart city.
- Training and finetuning SOTA models like YoloV4, Faster RCNN, etc..
- Working on challenging real-life projects using state-of-the art trackers.
- Dataset creating, cleaning, ..etc
- Hands-on experience with Deepstream | Nvidia TLT.
- Hands on experience with using, modifying Gstreamer pipelines in Python.
March 2019 - May 2019
Research & Development Internship
Valeo
-I was a research and development intern at Valeo, I worked on autonomous driving and participated in CARLA challenge representing Valeo in the challenge.
-The challenge was mainly about using deep learning to allow a simulated car to pass a couple of scenarios which show how good the car is at autonomous driving and how good the car passes the scenarios.
Projects
Accident Detection
Using information obtained from tracker, I was able to detect anomalies by calculating pixel velocities and direction of vehicles moving and from that information a normal range of velocities and magnitudes was generated and any outliers were considered anomalies which cause or had accidents.
Vehicle Entering Restricted Area
Implemented with Python and OpenCV, using YoloV4 and Nvidia's Deepstream 5 NvDCF tracker.
Object Dropped/Removed detection
Implemented with Python, using background subtraction SubSENSE algorithm.
Vehicle Color/Type Classification Analytics
Creating analytics based on vehicle colors and/or type, for example, a video would be created of all the red cars that pass along a certain street. The analytics are extracted from a given long video of the camera's feed or a live feed from cameras, implemented using Yolo with a tracker on Deep Stream.
Violence Recognition
The graduation Project, implemented with Python, Keras and OpenCV, a deep learning model was created in which a pretrained VGG-16 model is followed by an LSTM to extract temporal features and then it was trained on those extracted features to decide if violence existed or not. A dataset was created and added on Kaggle, can be found here.
I presented the publication about it in the presence of leading researchers and scientists. Publication can be found here.
Publications
December 2019
Violence Recognition from videos using Deep Learning Techniques ICICIS 9th IEEE Conference
Courses
1. Andrew NG's Deep Learning Specialization
2. Udacity's Computer Vision Nanodegree
AI Technical Skills
Soft Skills
Languages
- Python
- Anaconda | Pip | Docker
- ML | DL | CNN
- Keras | Onnx | MXNet | Gluon
- Computer Vision
- Github | git
- Linux
- Deepstream5
- Gstreamer (Python)
- Google Cloud Platform
- Teamwork.
- Public speaking.
- Problem Solver.
- Self Learner.
- Fast Learner.
- Ability to work alone.
- Self-motivated.
- Arabic - Native
- English - Professional