Computer Vision in IoT Electronic Observation Tools
The realm of computer vision is steadily progressing, with the advent of technologies such as extreme resolution imaging and augmented reality.
Computer systems, in this day and age, are able to recognize and analyze patterns and points better than ever before, and a lot of that has to do with the growing demands of the global security enterprise, as well as the entertainment industry. The latter is utilizing computer vision more widely and companies producing entertainment based technologies are leveraging other advancements such as AI and augmented reality to better their functionality while offering the customer more.
However, it is the security industry that is really expanding the horizons of what is possible in terms of surveillance capability.
Computer Vision in Surveillance
The application of computer vision in surveillance comes as no surprise, since security and surveillance cameras need to identify and analyze patterns and markers more efficiently, in order to function better. This development and the integration of computer vision in surveillance as a whole, can be attributed to security agencies and companies who consistently demand quicker and more efficient identification and pattern recognition.
The IoT-based Application
Applying the concepts of computer vision can prove to be very effective, however, applying them in an IoT based network, is quite another. It is considerably more effective, since M2M communication can then be leveraged, and the identification capabilities can be increased exponentially.
Following are some of the applications of computer vision inside an IoT based framework:
- General Manufacturing: Not only can computer vision be applied to observe every aspect of the manufacturing process, but the principles can be used for procedural surveillance and observation of the supply chain.
- Plant Growth Monitoring: The growth of valuable plants, used for a variety of medicinal and research-based purposes, can be done in real time, throughout the developmental stages.
- Traffic Monitoring: The ebb and flow of traffic can be managed and controlled through a machine-based network, which evaluates congestion potential and adjusts the flow to actively prevent both jams and accidents.
The Deep Learning Advantage
Many gaps in the surveillance world were filled with the advent of deep-learning systems, which used algorithms that allowed machine intelligence to be multiplied exponentially, over time. The best aspect of these algorithms was that they could potentially be applied to any number of systems, from the artificial intelligence in video games, to security systems that learn typical patterns over time, and highlighted them in real time, on the very next occurrence.
Also, deep learning algorithms, when applied to IoT-based frameworks, already demonstrated accuracy of judgment better than the human competition. This is a part of computer vision that, when engineered specifically for the surveillance industry, could bolster the possibilities for the industry itself.