Umama Rahman
April 27th, 2022
The data collected by most organizations no longer comprises merely rows and columns of textual values. A large part of it is based on visual data, that is, images and videos. The analysis of these visual data is critical for not only commercial purposes but also social causes such as public safety and health.
One of the scientific techniques dealing with image and video analysis is object tracking, which falls under the umbrella of computer vision.
Computer vision is a subset of computer science that, simply put, allows computers to see, comprehend, and analyze visual data. With computer vision, a machine can perform tasks such as image recognition and image classification, facial recognition, object detection, and, of course, object tracking.
Let’s talk about what computer vision object tracking is, how it works, and why your business should develop an object tracking solution.
Object tracking is the process via which computers are able to detect, understand, and keep an eye on objects across still images or videos. It is one of the most widespread business applications of artificial intelligence (AI) and machine learning (ML), enabling your visual data processing needs to be automated and streamlined to maximal levels. The underlying deep learning algorithms take inspiration from our biological nervous system to form a layered yet intricate network of data transmission and learning capacities.
With object tracking solutions, you can perform meaningful actions on visual data obtained via different types of cameras. Using suitable object detection algorithms coupled with object tracking models, you can train a machine to not just recognize one or more unique objects or persons in a particular image, but also identify them in subsequent frames and follow their trajectory in a video stream.
Some object tracking applications might require the program to analyze images or videos offline in batches. However, in time-critical scenarios, you would want your app to process the data in real-time. Therefore, over time, the need for object tracking algorithms and methods to become more efficient and accurate has grown.
There are two main types of object tracking: image tracking and video tracking. However, object tracking activities can also be categorized on the basis of operational levels, i.e. single and multi-object tracking.
Image tracking has more to do with identifying, anchoring, and tracking entire 2D images in virtual three-dimensional environments. A common example of image tracking can be found in the field of augmented reality (AR). You might have seen it in action when shopping online and virtually checking out how a new couch you’re thinking to buy might look in your living room.
Video tracking, on the other hand, is purely about tracking objects in videos.
The machine first identifies a particular object in the first frame of a video. Then, it analyzes the video frame by frame to identify the same object and trace its trajectory across the video.
Most of the better-known applications of object tracking, as you will read later on in this article, are examples of video tracking.
Single object tracking (SOT), as its name suggests, tracks one specific target object throughout a video. It is often seen in applications where the observer is only focused on one unique object and needs to ignore all other objects in the same environment.
The second level, multiple object tracking (MOT), is more complex since it involves the detection and tracking of more than one object in a video. It is also known as multi-target tracking (MTT). A typical multiple object tracking algorithm identifies all objects of interest from the initial frame(s), puts them in bounding boxes, assigns them all unique coordinates, and then follows the movement of these bounding box coordinates over back-to-back frames. The need to plot the unique trajectory of each detected object makes MOT more challenging.
Every business might have an exclusive need or use for visual object tracking systems. However, to better understand the practical application of object tracking algorithms, we have gathered some of the most common, easily comprehensible use cases.
With the help of sophisticated algorithms that are capable of realtime tracking of objects in a video, businesses can significantly enhance their security departments.
The video obtained from security cameras may be used in conjunction with other types of data such as those obtained from motion or IR sensors. These security data can then help the AI system identify and track the motion of suspicious items or people within a certain monitored location and automatically send out relevant alerts to the concerned authorities as well.
In light of the global pandemic, many businesses also implement object tracking programs for crowd monitoring as well. These have proven very effective in detecting whether people have been following SOPs such as maintaining social distancing or wearing masks when out in public.
With such a comprehensive system of automated surveillance powered by video tracking, not only can businesses benefit from better protection and great cost-saving but public health and safety can also be ensured while staying at a safe distance from dangerous situations.
A greatly innovative practical application of object tracking in the retail industry can be seen in Amazon Go stores. Amazon has created an amalgamation of various computer vision and artificial intelligence processes to introduce a cashierless checkout system for their supermarket:
Sensor data aggregation
Accurate camera calibration
Person detection and tracking
Object recognition
Pose estimation
Intent and activity recognition
There are a plethora of cameras placed across the store’s ceiling that are tasked with tracking each customer’s journey – from entry to exit – as they walk through the store.
The program not only identifies each item that a unique customer picks up but also takes note of whether those items go into the cart or back on the shelf.
At the end of the grocery trip, the customer gets an auto-generated receipt via their Amazon account which they can easily pay online.
This model helps customers save precious time by not having to stand in long checkout lines and waiting for their turns just to pay for their purchases.
Amazon, on the other hand, has benefited in more ways than one. It has successfully cut labor costs by eliminating the need for cashiers and stocktakers. Plus, ever since they introduced these cashierless stores, they have also cut costs by over 90 percent through investing in more sophisticated technology along with making other smart business decisions.
While a lot of people might not know of the crucial role that object tracking plays here, self-driving cars are perhaps the most well-known application of AI-driven object detection and tracking.
Let’s take a look at another popular computer vision project, Waymo (formerly known as Google’s self-driving car). The following video shows how Waymo vans use object tracking for various functions such as obstacle detection, pedestrian detection, trajectory estimation, collision avoidance, vehicle speed estimation, traffic monitoring, and route estimation.
There seems to be a bit of apprehension expressed by the general public when asked about driverless cars. However, the reaction is not much different from when, in the 20th century, “driverless” (operatorless) elevators were first introduced. Advancements in autonomous vehicles seem to be proceeding steadily as big and small businesses strive to achieve greater accuracy and build safer mechanisms down the road (pun fully intended!).
Multiple studies over the past several years have proved computer vision object tracking to be a key contributor to the improved success of modern treatments, surgeries, and other critical procedures in the field of medicine.
For example, object tracking has made it possible for medical practitioners to conduct more accurate video-EEG tests to remove background clutter and track even the slightest of jitters in patients under observation. The object tracking algorithm can then also successfully establish meaningful relationships between the observed video data and its medical implications to produce accurate EEG results.
Object tracking systems are expected to make valuable contributions to other various medical applications such as computerized semen analysis, robot-assisted procedures, cardiac surgeries, and aneurysm clippings.
Monitoring the medicine production process with object tracking
The pharmaceutical industry is also greatly benefiting from computer vision and object tracking.
Using both single and multi-object tracking, medicine production processes can be monitored in real-time to ensure that safety protocols are adhered to and any emergencies, such as machine malfunctioning, faulty medicine production lines, or chemical accidents can be detected and addressed in real-time.
While there are many off-the-shelf object tracking solutions and platforms available in the market, it can be difficult to choose one with zero technical knowledge. For example, common challenges and advantages of object tracking might include:
With a prebuilt solutions you might miss those challenges and struggle implementing object detection and tracking solutions. Whereas a good AI partner can solve those challenges and turn them into advantages, such as:
On top of that, these offers may not be optimized to perfectly suit your needs. They often come pre-loaded with cumbersome features that you might not need. Some of these apps might require highly advanced (read: super expensive) hardware.
All these factors end up affecting the results you aim to achieve from your object tracking solution. A custom-built solution, among many other advantages, will yield the most accurate results for your problem.
Working closely with your company, we will identify the key areas where AI can bring the most value. This involves meetings with all stakeholders and developing a roadmap for action together.
We will evaluate if you have the business data needed to train relevant machine learning models. If required, we identify additional frameworks for data collection in your company.
Based on our meetings and data analysis, we’ll share with you the possible AI use cases for your company. We will work hand-in-hand to agree on the desired outcome.
We will build and apply various machine learning models to your business data, to find the best solution. As a result, we will develop algorithms that accomplish the desired goal
We integrate the machine learning model with an API or front-end product, making it user-friendly and accessible to the end-user.
Any system might require time to time maintenance, and we are happy to support our customers with that.
With a customized solution, your software partner will understand your business and technical requirements and help you figure out the best way to solve the problem. Several meetings may be held to ensure that both you and your provider are on the same page. You can discuss your budgets and deadlines, and agree on project deliverables.
A good development partner will accommodate any special needs you might have, and you can also decide on the level of engagement you are willing to offer over the course of the project.At this stage, you should also communicate your needs about putting together a team for your project.
A basic team would start off with a project manager, an analyst, and a data scientist experienced in computer vision tasks.
A machine learning engineer is included when required by the deployment and inference performance requirements. With growing complexity, all of these specialist groups would grow and, in some cases, software engineers might need to be added to the team. Once the agreement terms have been addressed and decided upon, the development team will get to work.
This is perhaps the best part about going for a custom solution. A custom object tracking solution for a custom problem often leads to better results.
While this process might take a bit of trial-and-error, the team will build an object tracking model that performs as per your expectations and requirements, with the highest possible accuracy levels.
A reliable and responsible development team will also ensure that all the work done throughout the project is carefully documented.
This is crucial when building business apps since you, being the owner of the source code, might want to make changes to your app in the future with or without the help of an outsourcing partner.
Whether the end-users of your object tracking application are businesspersons or customers, they will easily be able to use the application as it will be deployed via a highly user-friendly interface. You can choose what features you wish to interact with via the GUI, minus the clunky, irrelevant, overly complex components.
With a bespoke object tracking solution in place, your business will benefit from high accuracy results, improved productivity and efficiency, better resource and time allocation and management, and worthwhile ROIs in the long run.
