Object detection is a basic task in computer vision, but not an easy one: this course prepares you to successfully incorporate all the secrets of this technique to your professional projects" 

When designing and programming a computer vision machine, it is essential to handle a series of tools and basic tasks so that the device works correctly and according to the established objectives.

One of the most essential is object detection, which allows the machine to identify elements, analyze and classify them. Therefore, something that may not seem so complex can actually be a fundamental element in the field of computer vision. 

This Postgraduate certificate in Object Detection in Computer Vision provides the professional with the latest knowledge and tools in this field, delving into issues such as occlusions, object detection evaluation metrics, motion detection, particle filters, object tracking, the computing platform or the choice of framework to be used in the project, among others. 

In addition, this program is carried out following an innovative, 100% online teaching methodology, that will allow the student to balance their studies with their professional career without any problems. It will also have numerous multimedia educational contents such as practical exercises, visual explanations on video, master classes or interactive summaries, among others.  

Specialize in object detection and develop interesting computer vision projects thanks to this program" 

This Postgraduate certificate in Object Detection in Computer Vision contains the most complete and up-to-date program on the market. Its most important features are:

• The development of case studies presented by experts in Deep Learning, computer science and computer vision
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Artificial intelligence is the future: learn more about the particularities of object detection and progress professionally in the field of computer vision"   

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.  

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.  

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.    

TECH innovative 100% online teaching methodology will allow you to combine your professional career with your studies"   

Achieve the professional progress you are looking for in the field of computer vision thanks to this Postgraduate certificate"

This Postgraduate certificate in Object Detection in Computer Vision is structured in 1 specialized module, through which the student will be able to delve into the accuracy of object detection, methods such as sliding window, evaluation metrics, the Viola detection method, particle filters, detection and tracking of people or re-identification, among many other issues. 

The most in-depth and cutting-edge content in object detection is here"  

Module 1. Object Detection

1.1. Object Detection and Tracking

1.1.1. Object Detection
1.1.2. Case Uses
1.1.3. Object Tracking
1.1.4. Case Uses
1.1.5. Occlusions, Rigid and Non-Rigid Poses

1.2. Evaluation Metrics

1.2.1. IOU - Intersection Over Union
1.2.2. Confidence Score
1.2.3. Recall
1.2.4. Precision
1.2.5. Recall Precision Curve
1.2.6. Mean Average Precision (MAP)

1.3. Traditional Methods

1.3.1. Sliding Window
1.3.2. Viola Detector
1.3.3. HOG
1.3.4. Non-Maximal Suppression (NMS)

1.4. Datasets

1.4.1. Pascal VC
1.4.2. MS Coco
1.4.3. ImageNet (2014)
1.4.4. MOTA Challenge

1.5. Two Shot Object Detector

1.5.1. R-CNN
1.5.2. Fast R-CNN
1.5.3. Faster R-CNN
1.5.4. Mask R-CNN

1.6. Single Shot Object Detector

1.6.1. SSD
1.6.2. YOLO
1.6.3. RetinaNet
1.6.4. CenterNet
1.6.5. EfficientDet

1.7. Backbones

1.7.1. VGG
1.7.2. ResNet
1.7.3. Mobilenet
1.7.4. Shufflenet
1.7.5. Darknet

1.8. Object Tracking

1.8.1. Classical Approaches
1.8.2. Particulate Filters
1.8.3. Kalman
1.8.4. Sort Tracker
1.8.5. Deep Sort

1.9. Deployment

1.9.1. Computing Platform
1.9.2. Choice of Backbone
1.9.3. Choice of Framework
1.9.4. Model Optimization
1.9.5. Model Versioning

1.10. Study: People Detection and Tracking

1.10.1. People Detection
1.10.2. People Monitoring
1.10.3. Re-Identification
1.10.4. Counting People in Crowds

A unique, key and decisive training experience to boost your professional development”