There Is A Trend Showing Out of The PCA Here!!

As we have discussed that Video Analytics is on the rise and still relatively new.

A good way to find out what others are doing with regards to detection and recognition technologies is to see what customers wanted to solve. and to achieve this, we can check out job requests from rentacoder.com

Most of the requests revolve around the following mentioned techniques to achieve desirable results:

• Mathlab
  
• Principle Component Analysis
  
• Pictometry API
  
• OCROpus
  
• Neural-network (backpropagation and quickprop algorithm)
  
• OpenCV
  
• Libface

There is a trend showing out of the PCA (Principal Component Analysis) here!!

As you can see that the biggest challenge of image recognition or just any kind of detection, recognition technology is that developing from a top-down approach is simply quite impossible due to a few reasons:

1. The scope is too big and fairly undefined.
   
2. The time-frame is too vague
   
3. The risk is simply quite unpredictable

Therefore, human beings are currently resorted to doing the bottom-up approach (based on specific problems). For instance:

1. Video analytics is a problem and has many sub problems (intrusion detection, behaviour analytics, object detection and etc).
   
2. Image recognition is a problem and has many sub problems (facial recognition, license plate recognition and etc).

And each of these problems requires intelligence and the sum of solutions to all these problems results in the formation of a subset of artificial intelligence.

This trend is more economical and the race is on for those who can edge over others by the doings of:

1. Making money and surviving out of providing solutions for specific problems.
   
2. Producing technologies which are more generic and can be applicable to other problems.
   
3. Software architecture which caters for modularity and scalability.

Conclusion:

Start with a specific problem and sell the technology. If there is no market for it, you might want to consider changing to a new problem by reusing the same works that you have done.