Humans, throughout the history, have shown a tendency to evolve and come up with solutions to the problems they face, using whatever resources they have. This can be seen right from the stone age to the present times. The use of stone implements for hunting, the discovery of fire and even the invention of wheel, were all solutions to problems faced by mankind. What is interesting in all these cases was the innovation that came along with these advancements, these innovations occurred inspite of limitations in technical understanding. This is to illustrate how, with the right implementation of acquired knowledge and available resources the most innovative products and solutions have been developed and how these developments have changed human life. With this thought in mind it becomes pertinent to examine the scope of solutions Video Analytics, integrated with machine learning, deep learning and artificial intelligence algorithms, has to offer.
Why Video Analytics?
- Technology Advancements: Conceptually Video Analytics is based on the techniques of image processing. Digital image processing as a concept has attracted active interest of researchers since the 1960’s. Now after close to 60 years of dedicated research, significant advancements have been made in the area of image processing. The knowledge is ready to be put to use.
- Infrastructure: The infrastructure required to support the technology and solutions e.g. in the form of integrated ip based cctv cameras, high speed communication protocols etc, is present in almost all domains. Almost all important physical infrastructure entities are equipped with these cameras for the purpose of surveillance, however in the present situation the presence of cameras might not create a significant reduction in the violations since this arrangement is post-mortem in nature.
- Abundance of opportunities: There is great abundance of industrial problems, waiting to be solved, in industry and in other walks of life. From solving security concerns to providing video enabled solutions to smart cities, video analytics can even revolutionise security and surveillance in manufacturing units and mining areas.
- Limitation of human intervention: The scale at which industrial operations are expanding, manual intervention will not be able to serve as a good way of monitoring the entities.
Challenges in developing solutions.
- Identification of problems: The fundamental issue in developing solutions arises from the inability to identify areas where these solutions can accurately work. Ideally any area or operation, where there is a dependency on visual intervention, should serve as a problem to be solved. However, the real life scenarios areas which require visual intervention are extremely non-linear and unpredictable. This creates a sense of technology handicap in developing solutions. The conventional way of solving digital image processing problems has a great deal of dependency on standard data sets obtained from predictable systems.
- Implementation on irregular data: Video Analytics fundamentally uses comparison between data points to arrive at a conclusion, the absence of standard data sets also creates an issue. This is the primary concern in systems like Automatic Number Plate Recognition(ANPR).
- Conventional Methods: Although significant improvement has been made in the area of image processing algorithms, conventional strategies of SIFT (scale-invariant feature transform) and SURF (speed up robust feature) have great dependencies on key points to create descriptor vectors. This starts creating issues since there are limitations these algorithms are prone to while working on non standard data sets.
- Integration: To be able to take giant leap in the domain of video analytics, integration of machine learning and artificial intelligence techniques should be done with image processing algorithms.
- Standardisation: The primary concern of non standardised data sets can be addressed in two ways, first would be coming up with predefined data points, especially in problems related to ANPR. The second method would be introducing artificial intelligence in the system so that irregular data points can be bundled together and logical sense can be derived from them.
- Multidisciplinary Approach: Multidisciplinary integration of software and hardware capabilities is inevitable in developing security critical systems. To be able to design accurate and robust solutions hardware capabilities have to be integrated with software capabilities to achieve desired results.
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