It is no secret that the opportunities for artificial intelligence (AI) are endless, and AI is becoming increasingly utilised in day-to-day lives. One of the biggest emerging trends is the progress of AI video analytics.Using CCTV and other videos, AI video analytics uses machine learning to make sense, analyse and provide deeper information for visual images. We can already see organisations utilising AI video in applications such as:
- Facial recognition
- Sports tracking
- Fault detection in factory lines
- Healthcare procedures and surgical operations
- Brand and media recommendations/targeting
- Safety and security applications
For such a vast range of applications, there are many different AI video systems, and each will offer a different way to process images. Some of the technologies the systems can use include:
Object detection: This can recognise objects in an image or video and correctly mark the items. Analytics can stretch further by also counting the items, localising them, identifying them and registering their exact positions.
Trigger alerts: This type of analytic is ideal for detecting unusual behaviour to help improve situational awareness. For example, there may be count-based alerts when a capacity is reached. They can issue a similarity warning when there are cases of appearance resemblance, which may require a surveillance response. Alternatively, facial recognition alerts can identify offenders and alert authorities in real-time.
Object recognition: using deep learning and machine learning, AI can quickly learn to spot objects to identify them and the surrounding visual information for greater understanding and analysis.
Object tracking: As well as recognising objects, there are technologies to track objects throughout video frame sequences. An example of this is tracking football players during a match to provide statistics on their performance, e.g. kilometres travelled and successful passes.With these technologies, the system will then apply deep learning, statistics, pattern recognition and neural networks, or a combination of these, which can help detect, identify, classify, track, retrieve data and forecast.With endless potential, there is a stark divide between those excited by the future of AI and others that have concerns. This is especially true when considering the challenges that can arise from the masses of data needed for deep learning. So, is AI analytics something we should fear for the future or something that can deliver a whole new level of benefits across a huge range of applications?
The Benefits Of AI Video
With its deep learning potential, AI video can offer a considerable array of benefits. For a start, the complex algorithms these systems can utilise mean that the whole process is incredibly comprehensive.Instead of requiring human resources, AI analytics can track a huge range of video surveillance systems or video streams at one time. What’s more, these systems can review each image pixel by pixel. The scale and convenience of this can far outweigh the alternative of human analysis.What’s more, as the system continues to learn and process more data, it improves its accuracy and ability, meaning that it can be applied to a greater number of applications while also potentially requiring less human intervention.
The Challenges Of AI Analytics
Data collection
In order to conduct the deep learning that make these systems so effective, they require a huge amount of data. This data needs to be provided, and some feel there are challenges around safe and consensual obtaining of the data.
Data storage
Due to the huge volumes of data, there are challenges around storing this data safely. Cloud-based solutions become essential for volume, but in regards to consent, some data may need to be stored in an organisation’s location. For AI contractors to organisations, it may mean organisations have to have a dedicated IT security team to safely manage data storage.Another challenge with this is GDPR and ensuring all data meets the necessary GDPR compliance. In some cases where video analytics are used for statistics, alerts or anomaly insights, GDPR may not be a concern. However, GDPR consent will have to be considered for individual-specific insights such as customer recognition.
Cyber security
There is a growing number of cases regarding IT hacking and threats to cyber security. As data increases with AI deep learning, there is a greater risk of large-scale internet breaches, which can threaten your operations, systems and customer data. This can have a huge effect on businesses and create a negative brand image.
Human input
The other main challenge for AI is that it requires human input to grow and develop. Human resources will be needed to handle the knowledge AI video analytics can provide, as well as improve deep learning with human input.AI training is essential for teams to effectively utilise AI video systems and make the most of the data they can process. At TSG Training, we help organisations to see the exciting future ahead for AI, but also the challenges organisations need to consider.To find out more about how our
AI training courses can support your business with greater data analysis and enhanced experiences, get in touch with our team today to find the right training course for you.
