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Motion algorithms are changing the pace and place of AI Computer Vision application development


26th October - Valencia.

In modern business it is becoming more and more necessary to solve tasks fast and accurately with less spending and human involvement, and this is where computer vision is coming to the fore.

Its operation is based on special image processing units that accept visual input then using algorithms perceive the image at the pixel level and helps the computer quickly read the "code" of the picture and give it meaning.

Complex algorithms already existed for motor and motion control, but they could only be used in high-end expensive applications due to the lack of small, powerful microcontrollers. Now these algorithms are going mainstream, and it's become feasible to use them in small embedded systems. Engineers are now focused primarily on the application, so their primary processing tasks are on the application level, such as image recognition, visual computing, encryption/decryption, and artificial intelligence.

By trying to replicate complex human vision, artificial intelligence, deep learning and neural trends in training algorithms make computers efficient enough to surpass human excellence in detecting and labelling objects.  The data generated is used to train computer vision motion algorithms in a better way and therefore improves computer vision performance.

Due to recent progress, AI computer vision has now found its feet in various different industries, such as education, healthcarerobotics, consumer electronics, retail, manufacturing, and more.


Motion tracking and detection 

One of the key growth areas is associated with motion tracking and detection. Motion tracking is an extension of object recognition, which it relies upon to locate the objects and their initial positions, and then follows their movement throughout the video, recognizing them as the same objects.

There are several algorithms for motion tracking, depending on the exact situation, such as whether the objects are 2D or 3D, or whether the camera is also moving. “Background subtraction” methods are often suitable for the most simple use cases, such as when objects are not moving too quickly. If two objects in two different frames have significant overlap, then the model considers them to be the same object.

At Tesla, for instance, motion tracking algorithms help drivers steer the car, accelerate, and stop the car automatically within its lane. Lane detection deep learning helps navigate lane changes and searches for the right parking place at the end of the ride to park the car automatically.


Motion Trajectory Planning

More advanced motion tracking algorithms attempt to calculate a person’s or object’s trajectory, or account for prior information about the person or object, in order to improve the model’s accuracy. For example, if the model knows it is tracking a person, it can limit itself to searching for “human-like” shapes in the next frame.

Hydranet is used by Tesla to handle a car's routing and behaviour in an actual trajectory situation and It leverages only what's needed by the system, ie. The lane line detection algorithm won't necessarily use the data from rear or side cameras.

Motion algorithms are now being developed to improve the human's understanding of a robots’ reachability where trajectory planning algorithms are also becoming important for robotic use in healthcare and manufacturing.


Other uses include:


Safety and security AI: Monitoring suspicious individuals and vehicles at the perimeter of sensitive areas.

Retail AI: Following the motion of shoppers throughout the store in order to track their behaviours, browsing patterns, etc.

Geospatial AI: Counting the number of animals in drone footage for purposes of ecology and conservation


Motion Prediction

Motion prediction has found its way into a variety of areas to replicate how humans use reasoning in order to predict what might happen in the future and make corresponding decisions. These algorithms primarily focus on the prediction of changes in the dynamics of the observed agents.

Examples include autonomous driving, surveillance systems, scene understanding, weather forecast and many others.

To enable working towards a single prediction model, Tesla uses eight cameras installed in the car system for sensing the vehicle's environment. Cameras are involved in taking pictures, sending them to the computer vision software and for processing in real-time. Ultimately, it helps find road edges, traffic signs, and distance estimation from other cars and pedestrians.


Pathfinding and path planning

Motion algorithms have also become important in helping where there are natural disasters when there is always a sudden and quick action needed for saving lives and properties.

Companies like Omdena are using CNNs (Convolutional Neural Networks) to develop a risk map and a pathfinding algorithm to find the shortest and safest way for an escape route solution for earthquakes.   

Path planning is also one of the most crucial research problems in robotics from the perspective of the motion control engineer. It has been applied in guiding the robot to reach a particular objective from very simple trajectory planning to the selection of a suitable sequence of action.

Path planning problems may also appear in complex 3D environments involving manipulation of sophisticated objects. For a dual-crane lifting problem, for example, using a multi-objective path-planning algorithm, generates optimized paths for lifting the objects while relying on an efficient algorithm for continuous collision detection.

Motion control will continue taking over many more applications, tasks, and needs within the personal and industrial environments in an unnoticed way. This will increase requirements on the quality of motion control in terms of safety, reliability, efficiency, accuracy and precision.

Adding intelligence into devices requires a different skill set, and finding enough qualified people is becoming a challenge. Due to the increasing demand for speed, accuracy, remote possibilities and affordability, the future and scope of AI Computer vision is on the rise driven by motion algorithms. It needs global specialists like CIS to find the right skills to fulfil expectations and opportunities. Their 20 years’ experience of placing experts can be called upon to develop your next applications. Call CIS specialist Hollie Webber now on +34 960 038 627 to make sure you are on the right track.