Video Analytics September 2022 CourseBy IPVM Team, Updated Apr 11, 2022, 03:00pm EDT
Register Now, registration cost is $299, course starts next month, September 6th.
Understand how video analytics work, what problems they have, and how to responsibly sell or deploy. This course is for anyone who currently or plans to manage, design, sell, or support video surveillance systems using video analytics.
This is the only independent surveillance analytics course, based on in-depth product and technology testing.
Lots of manufacturer training exists but none really teach the underlying technologies and tradeoffs of current 2022 analytics. This IPVM course solves that problem.
Based on IPVM's continuous, unique testing program of dozens of video analytic products, we show what really works and how it works today.
We also offer the Video Surveillance Analytics Course On-Demand.
The Video Analytics course will meet live online, Sep. 6th - Oct. 20th, 12 times over 6 weeks (Tuesday and Thursday for 1 hour from 10am ET to 11am ET).
In 12 live sessions, over 6 weeks, we examine the following topics in-depth:
(1) Fundamentals 1:
We explain the basics of how video analytics work, including basic image analysis, and the 4 core analytics categories used in video surveillance: VMD, Heuristics, Conventional Object Detection, Deep Learning Object Detection. Additionally, we will examine the pros and cons of each category.
(2) Fundamentals 2:
We introduce deep learning neural networks for video analytics, how neural networks are structured, and the most common open-source neural networks used in video surveillance (e.g. YOLO, SqeezeNet, Resnet, etc.). We will also examine datasets for neural network training (e.g. COCO, ImageNet, Pascal2, Wider, Government datasets), and explain the pros and cons of each, and issues related to bias and ethical challenges.
(3) Measuring Accuracy:
We examine how video analytics accuracy is scientifically defined, including ground truths, false/true positive/negatives, and how they relate to real-world video surveillance performance. Additionally, we will explain how manufacturers define accuracy, simplify their accuracy marketing, and how it is often misleading.
(4) Accuracy Problems:
We explain common problems that break video analytics performance including logistical and practical challenges of large face watch lists, which often cause significant false recognitions in facial recognition. We examine how that relates to average precision and what that means for using facial recognition in access control.
We explain the 9 most common video analytic architectures (e.g. All-in-camera, All-in-recorder, cloud, etc.). We examine the pros and cons with each, including common accuracy issues, cost comparison, and network bandwidth requirements.
We explain the 4 most common video analytic hardware devices (CPU, GPU, VPU, SoC), and examine the common strengths and weaknesses in each, including cost, accuracy, and efficiency. We also explain the most common providers (NVIDIA, Intel, Huawei, etc.) and how each approaches video surveillance.
(7) Person / Face / Vehicle:
We explain how the 3 most common video analytics work (Person, Face, and Vehicle), and most common accuracy problems, and the logistics and practical challenges of performance. We also look at specific metrics that most impact face detection (angle of faces, lighting) and the fundamental difference in face detection and facial recognition.
(8) Advanced Objects / Behaviors:
We explain how the most common behavior analytics (e.g. Intrusion, Loitering, Tampering, etc.) work, and the logistical and practical challenges that cause them to break. We explain how different analytics (VMD, Machine Learning, Deep Learning) impact performance, and what common problems can be avoided (e.g. Poor lighting, the incorrect field of view, etc.)
(9) Facial Recognition:
We explain how facial recognition works, how it is trained/programmed, and what logistical and environmental challenges cause accuracy problems (e.g. camera angles, uncooperative subjects, masks, etc). We also examine common neural networks and datasets that are used by video surveillance manufacturers.
(10) LPR / ANPR:
We explain how LPR/ANPR works, and different analytic types (OCR vs Deep Learning), and how they impact performance and efficiency. We also explain the 5 most common issues/challenges to LPR accuracy (speed, angle, weather, lighting, plate designs).
We examine the most common demographics for people (e.g. gender, clothing, hair, glasses, etc.) and vehicles (e.g. type, color, make, model), and the most common problems. We will look at what features or details video analytics detect for making common classifications.
(12) Providers / Market Overview:
We examine the performance of specific video analytic offerings (e.g. Avigilon, Axis, Dahua, Hikvision, and more), and how video analytics are commonly sold in video analytics (in-camera vs software vs appliances vs cloud). We conclude by examining the future impact of video analytics on video surveillance.
Who Should And Should NOT Take This Course
This course is intended for those who want to manage, design, sell, or support video surveillance systems using video analytics. Completing this course enables you to understand how these analytics work, what problems they have, and how to responsibly sell or deploy these analytics.
This is NOT a software development course nor an academic course in computer vision. If you are interested in building and training your own analytics, this is not the right course for you. Consider watching the Stanford Computer Vision course videos.
The course will be led by Sean Patton from IPVM. In addition to heading up IPVM's Video Analytics Course, Sean Patton also leads video management reporting at IPVM, analyzing and testing products to determine which are best and worst at various capabilities, and what companies are emerging. Previously, Patton designed systems as a security integrator and graduated from the Rochester Institute of Technology.
Additionally, all classes are recorded so you can watch on-demand online anytime.
At the end of classes, you will take a cumulative final exam including multiple-choice and short answer questions. If you pass, you will earn an IPVM Video Analytics certificate of course completion (see list of current IPVM Certificate Holders).
See what previous students said:
We send Video Surveillance Analytics students a survey where we ask, "How has this training helped you with your job?"
Here are some samples of responses:
"We are installing a new camera system and this course has helped me keep a more measured opinion of how cameras will be installed and what to actually expect with camera analytics."
"I have a more clear understanding of video analytics and can better evaluate systems. I feel armed with the knowledge to ask better questions when a provider reaches out to us to sell their product."
"It helped me get a better understanding of how integrators view analytics in today's market."
"I am more aware of the capabilities and limitations of video analytics."
An active IPVM subscription is required to take the course.
We also offer the Video Analytics Course On Demand here.
If you have any questions, please ask in the comments or email us at firstname.lastname@example.org
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