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Video Stream Processing – Securing the Future

With the rapid development of urbanization and the advent of ever new technology, there has been an explosion of data all around us. From satellites to mobile devices, websites and social media, surveillance and IoT devices, every source is generating a vast amount of unstructured data, videos, and images every second. More research is encouraged on computing devices.

 It proves to be a challenge to manage and analyze such mammoth amounts of data efficiently. For instance, it would be impractical to monitor the video stream of every camera within a city’s video surveillance network to be able to gather objects and events of interest. Though dynamic data are available for extracting new living patterns and making urban plans, efficient data processing, and instant decision making are still the key issues.

Analytics harness the power of big data and enable enterprises to make quick and better decisions, create innovative products, optimize their performance and through it all gain advantage in today’s markets. Video stream analytics is a method to source data and translate it into intelligent information.

Video Analysis and Surveillance

In today’s world, we encounter the camera everywhere – a sensor that is relatively inexpensive and highly used. By being able to extract information from the camera feeds, we can gain access to many of its real world applications such as automated surveillance and traffic control.

Video processing is computer architecture to build a system to retrieve high- level information from the camera feeds. It is applying intelligence to the Computer Vision field.

Traditionally in the video analytics systems, the CV library collects and processes data simultaneously. This was a major limitation of the system because in case of a server failure the entire video stream was lost. Switching to another mode of processing on detecting a node failure often resulted in fragmented video.

The use of big- data technologies was incorporated in video stream analytics to enhance end user experience as well as to optimize the application performance. Big data technology is used to process data from large scale video streams on demand and to retrieve a different set of information from the video stream and analyzing the data afterwards with different machine learning libraries. It is also useful in sending analyzed data to other components of application for further processing. A scalable system is required to efficiently process large scale video stream data.

Components of Video Processing

Video stream processing can be recognized as the combination of three main tasks;

  • Object detection

Just as a human eye can differentiate between two different objects, machines can also be trained to do so using object detection models, such as Tensorflow models and Open CV cascades. Trained model can be used to detect object in a video frame.

  • Object recognition

In object recognition, object detection is used in the initial stages and then the detected image is mapped into a known related sample dataset to match the features. It is widely used for face recognition and number plate recognition.

  • Object tracking

Object tracking is used to monitor object behavior and is considered a complex process. In tracking, object detection is the initial input and the object is tracked through the video. There are several tracking algorithms that can be used to track a detected object.

Applications for video stream analytics include three main components;

  • A video stream collector

The video stream collector is provided with the video stream data from the group of IP cameras. The video frames are sequenced to stream data buffer. This data is queued up for streaming video data. The serialized data is sent to the video stream processor from the buffer.

  • A stream data buffer

The data stream that is to be processed has to be stored in a temporary storage. In certain applications, the Kafka platform is used as a buffer queue for the data provided by the data stream collector. Using Kafka, the duration of time for which the messages are stored can be configured.

  • A video stream processor

There are several image processing libraries. Open CV and Tensorflow are some widely used programs. Processing is not a single task but a series of subtasks. A video is read frame by frame and image processing is applied to each frame using many filters, to extract features.

Conclusion

Video stream processing can take surveillance and monitoring to a whole new level, reducing time, money, and human effort. This makes the security process more reliable and consistent.

I am a blogger, typically dealing with imaginative and futuristic concepts of advanced science and technology.
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