Video Surveillance Analytics Course On-Demand Overview

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, by far, the best training on video analytics in video surveillance, based on IPVM's current 2021 testing and research.

Register now - The course costs $299.

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If you want to understand how AI and video analytics work in video surveillance, this course is a must.

Lots of manufacturer training exists but none really teach the underlying technologies and tradeoffs of current 2021 analytics. This IPVM course solves that problem.

Register for the Video Analytics Course On-Demand

What the IPVM Video Analytics Course Covers

In 12 on-demand video sessions 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.

(5) Architecture:

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.

(6) Hardware:

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).

(11) Demographics:

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.

Examine the top providers of video analytics and how video analytics are sold

Certification

At the end of the course, you will take a comprehensive final exam including multiple-choice and essay questions. If you pass, you will become IPMVU Camera certified (see list of IPVM Certified Professionals).

More About IPVM Courses

Subscription Required

An active IPVM subscription is required to attend the course. If you do not already have a subscription, the course fee includes one month free subscription.

Registration Open

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Register now for the Video Analytics Course On-Demand.

Questions

Any questions, please ask in the comments or email us at info@ipvm.com