The visual data volumes are predicted to escalate to a staggering 180 zettabytes in businesses. This prediction is based on expert forecasts and predictions for the year 2024. The increase in visual data volumes is expected to have a significant impact on various sectors, including enterprise technology, cybersecurity, and AI. This underscores the rising importance of visual data in shaping decision-making and innovation across industries. However, managing this massive bundle of data is challenging. This is where object tracking with computer vision comes in.
Object tracking with computer vision is a branch of artificial intelligence that allows computers to sense and interpret their surroundings visually. This technology helps to identify, classify, and track objects in real time.
For example, in the retail industry, object tracking with computer vision monitors customer behavior and analyzes their movements around the store. Similarly, various industries have adopted these computer vision services due to their ability to identify and monitor individuals or objects in surveillance footage.
Let’s further discuss computer vision with object tracking—what it entails, how it operates, and its types and use cases in business.
What Is Object Tracking with Computer Vision?
Object tracking with computer vision means detecting and monitoring objects in images or videos. It needs object-tracking algorithms, such as optical flow, correlation filters, and deep learning, to enable real-time tracking of moving objects.
Object tracking offers accurate and reliable information regarding object movements, even when they are obscured, partially hidden, or change appearance.
It enables uninterrupted monitoring and examination of a particular object or area of interest. It also extracts pertinent features such as color, shape, texture, motion, or deeper characteristics to distinguish and track objects effectively.
Object tracking with computer vision employs various methods to determine the most appropriate approach for tracking an object or group of objects within a specific context.
Types of Object Tracking
Different approaches to object tracking depend on the algorithms and techniques used. Here are some common types to consider:
1. Single Object Tracking (SOT)
This algorithm focuses on following a single, particular object across a video sequence. This is ideal for scenarios with transparent objects and few obstructions.
EXAMPLE: Single-object excels at tracking individuals in videos or tracing a ball’s movement in sports footage.
2. Multiple Object Tracking (MOT)
Multiple object tracking involves the simultaneous tracking of objects, even when they interact or impede each other. This algorithm surpasses single object tracking (SOT) in effectiveness due to its ability to handle data association challenges.
EXAMPLE: It is used for security or in crowded scenes for traffic monitoring or pedestrian analysis.
3. Point Tracking
Point tracking tracks the movement of small, localized features within an object (e.g., fingertips, eyes). This algorithm is particularly valuable for analyzing intricate movements or changes in the shape of an object.
EXAMPLE: It is helpful for precise tracking, such as hand gesture recognition or facial expression analysis.
4. Silhouette Tracking
Silhouette tracking focuses on the object’s overall shape or outline instead of detailed features. This method is proper when the color or texture is inconsistent, as it tracks the object based on its silhouette.
EXAMPLE: It tracks pedestrians in crowded areas or vehicles in traffic.
5. Correlation Tracking
This object-tracking method compares the object’s appearance in one frame to that in subsequent frames to determine similarity. While simple and efficient, it is prone to challenges such as background clutter and object deformations.
EXAMPLE: It tracks objects in surveillance footage or monitors animals in wildlife research.
6. Kalman Filter Tracking
Kalman filter tracking techniques can predict the object’s future location based on its past movement and sensor data. This method excels at handling smooth and predictable motion with great effectiveness.
EXAMPLE: It is commonly used in tracking objects or vehicles in traffic flow analysis or predicting the trajectory of missiles.
7. Deep Learning-Based Tracking
Deep learning-based tracking uses convolutional neural networks (CNNs) to learn and track objects’ features. This method has significantly succeeded in complex scenarios where traditional algorithms may falter.
EXAMPLE: It is used for real-time tracking in self-driving cars or analyzing human behavior in surveillance footage and has numerous applications across different industries.
Business-Specific Use Cases of Object Tracking
Object tracking with computer vision automatically recognizes and follows objects in video or image sequences. It has provided numerous advantages across different sectors of business. Let’s explore some use cases.
1. Retail Industry
A tremendous, innovative, practical application of object tracking can be seen in the Farfetch London luxury store, which helps track high-value inventory in real time.
The system quickly identifies items as they move around the store, providing 99.5% accuracy and reducing manual inventory checks. It saves staff time and enhances customer service by enabling immediate product location and availability information.
It facilitates cashier-less checkout systems to bill customers without traditional lines. This offers a frictionless and efficient shopping experience.
2. Manufacturing Industry
Object tracking with computer vision has revolutionized manufacturing processes, particularly in quality control and asset management. Ford Motor Company employs sophisticated deep learning-based object tracking solutions to achieve an exceptional 99.7% accuracy in detecting defects in cylinder heads.
It tracks the movement of goods and equipment or monitors employee workflows to optimize operations and minimize downtime. As a result, manual inspections have been cut by 50%, and the defect escape rate has fallen by 15%, saving millions of dollars in rework expenses while enhancing product quality.
3. Transportation
Object tracking is crucial for self-driving cars to detect and track pedestrians, vehicles, and other objects on the road. It enables accurate perception and decision-making for safe and efficient autonomous driving.
The Los Angeles Department of Transportation (LADOT) has implemented a citywide traffic management system that uses an object tracking system for cameras and sensors to analyze traffic flow in real-time. This system has led to a 10% reduction in traffic congestion and a 15% decrease in travel times during peak hours.
4. Security and Surveillance
Tracking suspicious activity and individuals in real-time for security purposes is a critical application of object tracking. The New York City Police Department used object tracking with computer vision in its surveillance system.
They use it to recognize suspicious behavior or abandoned objects in public areas. It has been a vital tool in preventing potential threats and maintaining public safety.
5. Healthcare
Precise tracking of surgical instruments has improved surgical efficiency and reduced errors, which has led to better patient outcomes. For example, object detection tracking systems monitor the movement of surgical tools during robotic-assisted surgeries.
The system provides surgeons with real-time visualization of the tools within the patient’s body that refine surgical precision and reduce procedure time.
These are just a few use cases of object detection with computer vision. These applications continue to grow, and businesses can expect more innovative solutions.
Is Custom Object Tracking Right for Your Business?
Businesses opt for custom object-tracking applications because this helps them filter out irrelevant data and focus on the most valuable information.
This technology is adaptable and grows alongside your business. Its customization perfectly fits the specific business processes and workflows, making it a more efficient and effective solution.
If your business requires monitoring, tracking, or analyzing objects in any setting, custom object tracking could be the right choice for you. It will provide real-time insights and improve decision-making processes for better outcomes.
How Can You Develop a Custom Object Tracking Application?
1. Customization for Specific Needs
Customized object-tracking applications are designed to align with business needs and overcome challenges that are unique to them. With tailor-made solutions, you can address your specific pain points and improve your processes accordingly.
2. Data Collection and Preparation
The success of any computer vision application depends on the quality and quantity of data it is trained on. Custom object-tracking applications have the flexibility to collect and prepare data from various sources that include in-house databases or live streaming.
3. Integration with Existing Systems
Customized applications integrate easily with existing business systems and processes, reducing implementation time and costs. It ensures a seamless transition and minimal disruption to your business operations.
4. Cost-Effective Solution
You can avoid expensive licensing fees and unnecessary features by opting for a custom object-tracking application. This solution is tailored to meet specific requirements at a more affordable cost.
5. Scalability
As your business grows, its data and operational needs also increase. Custom applications can be easily scaled up or down per your business requirements, making them a flexible and long-term solution.
Every year, businesses gather approximately 1 petabyte of data, a substantial portion of which comprises visual data, while platforms like Instagram witness over 1 million image uploads per second.
These numbers are only expected to increase in the coming years, creating a massive demand for computer vision with object-tracking technology. Businesses across different industries are embracing custom object-tracking applications to optimize their processes, reduce costs, and improve overall performance.
Looking ahead, the future of this technology appears promising, with potential advancements anticipated in the areas of real-time 3D tracking and multi-object tracking. These developments hold the promise of enhancing precision and effectiveness across various industries.
Dawood is a digital marketing pro and AI/ML enthusiast. His blogs on Folio3 AI are a blend of marketing and tech brilliance. Dawood’s knack for making AI engaging for users sets his content apart, offering a unique and insightful take on the dynamic intersection of marketing and cutting-edge technology.
