Multi-Object Tracking enables deep learning processes in automotive manufacturing
Keeping track of packages and parts is a major challenge in logistics and manufacturing. Traditional tracking systems often fail in complex environments, causing delays, errors, and inefficiencies.
An ergonomic material handling manufacturer for the automotive industry recently experienced this with their hoisting systems. These systems are implemented with encoder-based tracking that is incompatible with most conveyance methods. As a result, they saw an increase in inefficiencies with car bodies not being tracked effectively, which has the possibility of leading to potential damage to the product.
The Challenge: Why Traditional Tracking Fails
The customer provides responsive systems for lifting and maneuvering suspended product. These augment human force input, allowing fewer people to perform manual tasks with ease while eliminating human error.
With these systems, the customer wanted to provide an encoder-less car body tracking system that integrates with their system for inserting a dashboard into a car body. They were looking to more accurately track car bodies, so the servo system only allows movement toward the correct car body.
The goal of this is to prevent damage by providing fine resolution positional feedback loop so the servo system only allows component insertion once it is in the correct location +/- 3mm.
Traditional tracking methods rely on technologies like encoders and manual video reviews, which are prone to errors, slow, and difficult to implement in dynamic environments. Companies often face challenges such as:
- Inaccurate package tracking on conveyors
- Time-consuming manual counting processes
- Difficulty integrating tracking into automated assembly lines
The Solution: Smarter, Faster, and More Precise Tracking
That's where Real-Time Multi-Object Tracking (MOT) comes in a camera-based solution that accurately tracks multiple objects at once. This deep learning application combines object detection and tracking techniques and reports in real time course and fine tracking information to the customer.
MOT uses high-speed cameras and AI-driven algorithms to track multiple objects at once, ensuring real-time visibility and accuracy. The system helps eliminate errors, speeds up processing, and seamlessly integrates into existing operations.
With the need for a precise tracking system to streamline dashboard installation on moving vehicles, SICK’s vision-based tracking solution addressed this need by integrating rough positioning to detect the car’s location on the assembly line, ensuring accurate placement.
It then uses fine positioning to align the gantry arm for precise dashboard installation. By replacing outdated tracking systems with real-time AI-powered object tracking, SICK helped the customer achieve improved accuracy and automation in their assembly process.
