Pose estimation is an application of computer vision in smart cities.
It can be utilized for mobility management-related functions such as traffic control, elderly care in old age homes and several others.
Smart cities—or any cities, or towns, for that matter—are unrelenting cauldrons full of people and continual movement. Whether you talk about vehicular or pedestrian movement, one thing that everybody will unanimously agree on is the importance of mobility management. Poor traffic and vehicular management are directly responsible for approximately 1.3 million deaths every year. Smart cities, with all the technological and connectivity-based tools at their disposal, can use computer vision to make different kinds of mobility within them as frictionless as possible. Pose estimation, a computer vision-driven application, monitors the movement of people, vehicles, and other moving entities in smart cities. Based on this monitoring data, smart city administrators can optimize the mobility of different kinds in an urban environment.
In this way, the use of computer vision in smart cities to aid urban mobility improves the livability quotient of such zones.
Accident Prevention with Vehicle Pose Estimation
To state the obvious, behavioral monitoring—such an integral component of pose estimation—is not applicable to vehicles. In fact, vehicles may appear to move in different ways based on different CCTV camera angles alone. Computer vision algorithms can be trained to predict where a vehicle is headed or its future speed based on factors such as its alignment, the various road signs, status of its parking lamps or side-indicators—whether they’re engaged or not—and similar other factors to estimate its position in the immediate future. If the cameras preempt the possibility of a collision between moving vehicles or a vehicle and a pedestrian, they can communicate this with the vehicles over a connected smart city network, which can then prevent the mishap from happening. This feature is particularly useful in blind alley situations.
In this way, involving computer vision in smart cities simplifies the task of traffic management, accident prevention, and vehicular mobility regulation.
Eldercare with Ambient Intelligence
Care homes are full of individuals with dementia and other old age-related mental conditions that may make them behave in unpredictable ways. Ambient intelligence involves sensors and computer vision cameras embedded into everyday objects for data collection. Without violating their privacy, such tools can be used to autonomously monitor them. Computer vision and machine learning algorithms can be modeled to detect potentially self-harming behavior and inform the concerned caretaker immediately.
Pose estimation serves to make the mobility of any kind smooth in a smart city. Machine learning and computer vision in smart cities simply make that necessity a possibility.
How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning
In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.
Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.
Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.
Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.
Power of Machine Learning for Root Cause Analysis
To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.
Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set.
Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.
Benefits of Using Machine Learning for Root Cause Analysis
There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.
The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.
The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.
Provides Long-Lasting Solutions
Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.
Grows Your Business
Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.
No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.
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