Deep learning has been a transformational force in changing what embedded vision can do for a vast array of applications. However, before we start, let’s first draw a comparison with perhaps a more familiar AI technology subset. With data analytics becoming integral, there’s a good chance you have heard about Machine Learning (ML) technologies.
To recap, ML is the ability of machines to recognize data patterns while constantly learning from them. Even though ML algorithms are not explicitly programmed, they still need predefined rules to perform specific tasks and understand patterns.
What is deep learning?
Deep Learning (DL) is a more advanced and sophisticated subset of ML. Based on an artificial neural network, DL algorithms demand far less human intervention. In a sense, the desired application outcome is provided as an input for the machine to work out the details of achieving them! It’s why DL-based applications rely on large-sized datasets in order to make intelligent interpretations in real-time.
The markets are certainly gung-ho about it. During this pandemic, the deep learning sector has taken giant leaps – compared to prior years. According to a study by Fortune Business Insights, “The global deep learning market size was valued at $6.85 billion in 2020 – projected to reach $179.96 billion by 2030 (a CAGR of 39.2%)”. So, definitely deep learning and associated technologies are here to stay.
What is a DL-powered AI camera?
When it comes to deep learning, many tend to limit the discussion to the software and algorithms used to implement DL/ML techniques. However, hardware components play an equally significant part in the form of collecting the necessary data for these computer applications or edge-based processors to analyze and make decisions based on. And smart AI cameras and embedded camera systems have been transforming this space by offering ‘smarter’ ways to collect image and video data accurately and reliably.
That said, let us look at what an AI camera is.
A deep learning camera (or an AI camera) is equipped with AI power to understand classifications like humans, vehicles, and more. Understanding such classifications, they have data-rich decision-making capabilities to detect movement and capture high-quality images while avoiding irrelevant information.
Major functions of a deep learning AI camera
A deep learning AI camera has integrated imaging features – including classifying, processing, and segmenting images. Following are some of the most important functions and capabilities of an AI camera:
- Image classification: As earlier addressed, AI cameras can automatically tag images to several classifications to identify and capture the right image at the right time. For instance, it can be used to detect damaged goods on the assembly line inside a warehouse.
- Image recognition: AI cameras can assign every pixel to a particular classification – thereby enabling quick identification of multiple objects on a single image. This feature enables instant object recognition – helping determine sizes, shapes, types, and other specifications of objects. An example of this would be automatically identifying objects dropped into a shopping trolley in an autonomous checkout system.
- Image processing: In a deep learning camera system, while the camera plays the role of capturing image data, high-end processors – like the NVIDIA Jetson series – process this information to derive inferences on the edge. This processed data can be right away used for automated decision-making.
Key embedded vision use cases of an AI camera
When the use cases of an AI based camera system are endless, for the purpose of brevity, let us look at some of the most popular ones here.
Smart traffic systems are crucial to modern urban planning as they prevent traffic congestion and overcrowding while significantly reducing the number of fatal accidents. Incorporating deep learning based image segmentation functionality, these systems can zero in on visual information in real-time. Hence, it becomes hassle-free for an AI camera to segment/track vehicles, estimate traffic density, monitor adherence to speed limits, etc. If you wish to learn the factors to consider while choosing a camera for traffic management systems, we recommend that you read the article Choosing the right camera solution for smart traffic management.
Performance is in the details as far as auto farming systems are concerned, because even the slightest misinformation can trigger loss of business or overspending. So, their AI camera solutions rely on powerful deep learning algorithms. The reason is that these AI cameras are expected to capture minuscule details like crop growth, soil quality, diseases, etc. and automate critical parts of the farming process. To develop a better understanding of the importance of embedded vision and cameras in farming, please have a look at Embedded vision in auto farming – here’s why camera selection is vital.
Automated sports broadcasting
AI cameras have flipped the script on how sporting events are broadcasted to audiences all over the world. From soccer and baseball to cricket and basketball, easy-to-deploy AI camera systems are being used to capture and stream live footage – with integrated network connectivity. In addition, smart cameras come with the ability to run deep learning algorithms to automatically analyze video streams – offering valuable player inputs to coaches and team managers.
To learn more about the role of embedded cameras in automated sports broadcasting and how to choose the best camera for it, please go through the article Choosing the right camera for automated sports broadcasting – everything you need to know.
Security and smart surveillance
The surge of smart cities has caused the demand for continuous surveillance to ensure citizen safety. Also, considering the boom of business infrastructure, ensuring 24X7 security in and around the premises can no longer be compromised. So, is it any surprise that AI camera solutions have become so popular in smart surveillance applications such as parking lot management and street monitoring? Smart AI cameras can capture images and videos to create security protocols by applying deep learning techniques – from monitoring and analyzing crowds with facial recognition to processing videos in real-time to identify threats and raise alarms.
The choice of sensor is very critical when it comes to these applications. To learn how to do it the right way, checkout one of e-con’s articles Choosing the right image sensor for smart surveillance applications.
e-con’s SmarteCAM: Your DL-led next-gen smart AI camera
SmarteCAM, designed by e-con Systems, is a ready-to-deploy AI camera with powerful deep learning capabilities. It comes with an on-board NVIDIA® Jetson™ TX2 CPU and 256 core GPU to ensure cutting-edge image processing. With the High Dynamic Range (HDR) feature, SmarteCAM provides images with exceptional quality even in low light conditions.
You can perform analytics indigenously without the connectivity or even the power of the cloud! SmarteCAM can run image-based ML and DL models at the edge – and is perfect for smart surveillance, traffic monitoring, parking lot management, crowd monitoring & analysis, and more.
Below is a video that gives an overview of the product’s features and applications:
Why e-con’s SmarteCAM is the smart AI camera for the future?
- SmarteCAM comes with an option to integrate 4 e-con cameras depending upon the end application and use case. These cameras are based on the sensors Sony STARVIS IMX290, Sony STARVIS IMX415, and AR0821 & AR0234 from Onsemi.
- This ready to deploy smart AI camera offers you the flexibility to program your own CV algorithms and AI models since SmarteCAM is a programmable OEM camera.
- You can use it in harsh outdoor conditions as it comes with an IP66 rating – making it rugged and dust/waterproof.
On SmarteCAM, you can run multiple AI algorithms with an impressive coverage of intelligent video analytics (IVA) applications. To know how this works, you can have a look at the below technical articles:
- How to run DeepStream applications out-of-the-box on SmarteCAM
- How to run the YOLO object detection on SmarteCAM
Further, SmarteCAM is an AWS certified device and offers you the ability to run AWS IoT Greengrass on it. To learn more about how this works, please have a look at the article How SmarteCAM works with AWS IoT Greengrass.
We hope this blog has given you some practical insights on why deep learning is no longer just a buzzword in the field of embedded vision. It’s something that your application may already require so that you can do more with imaging data than ever before. Ultimately, it can enable you to deliver experiential and deeply impactful application experiences.
If you are interested in evaluating e-con Systems’ programmable smart AI camera SmarteCAM, please place a query by visiting the product page. You could also write to us at email@example.com.
See you next week as our Technology Thursday series continues!
Prabu is the Chief Technology Officer and Head of Camera Products at e-con Systems, and comes with a rich experience of more than 15 years in the embedded vision space. He brings to the table a deep knowledge in USB cameras, embedded vision cameras, vision algorithms and FPGAs. He has built 50+ camera solutions spanning various domains such as medical, industrial, agriculture, retail, biometrics, and more. He also comes with expertise in device driver development and BSP development. Currently, Prabu’s focus is to build smart camera solutions that power new age AI based applications.