The primary goal of video analytics is the automatic recognition of spatial and temporal events in recorded or live videos. Not following traffic signs, suspicious movement of a person, and the sudden appearance of smoke or flames are a few instances of what video analytics can do.
Here, start having a look at how a video analytics solution works. Based on a certain use case, the architecture or form of such a solution may differ. However, the working process remains the same.
Experts or users can do video content analysis in two different ways - in real-time by the configuration of the system to generate alerts for particular incidents and events that are disclosed in the moment, or in the post process by conducting advanced searches for the facilitation of forensic analysis tasks.
Feeding the system
The data for analysis can come from several streaming video sources. Common sources are traffic cameras, CCTV cameras, and online video feed. However, any video source that uses the acceptable protocol (e.g. RTSP: real-time streaming protocol or HTTP) can generally be integrated into the answer.
The primary goal is coverage: we'd like to have a clear view of the complete area, and from several different angles, where the events being monitored might occur. Keep in mind more data is best for processing.
Central processing vs edge processing
As a user, you can run video analysis software on the servers located in the central monitoring station. The process is central processing. Or you can embed video in the cameras. This process is edge processing.
Your choice of cameras should be based on solution design. In the past, a lot of legacy software was present. In the current world, you can come across hybrid solutions. In reality, the good practice relies on concentration, real-time processing, and forensic analysis functionalities on the centrally located server.
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Due to a hybrid approach, the processing work of cameras lowers the data processing by the central servers. Otherwise, it could need substantial bandwidth and processing capabilities with the rise in the number of cameras. Further, it is possible to configure the software only to send data on suspicious events to the server over the network. As a result, there is a low need for data storage and network traffic.
At the same time, data centralization for forensic analysis allows several analysis and search tools to be used. The tools could be general algorithms and ad-hoc implementations. The utilization of several different sets of parameters is done to silence and balance the noise in the obtained results. You can feed your own algorithms to get the desired results that are certainly attractive and flexible schemes.
Scenario defining and training models
After planning and installation of the physical architecture, defining the scenarios that you want to focus on is crucial. Further, model training for the detection of targeted events is necessary.
The probable scenarios can be crowd flow and vehicle crashes. Video analytics can help to find and recognize the main culprit/s. Every scenario relates to a series of fundamental tasks that the system should know how to perform the same.
For example, you can take vehicle detection. Apart from detection, you have an idea about the types of vehicles (car, motorcycle, bus, or truck), movement in a particular direction, and the study of all allied things. Here are the most frequent basic tasks of video analytics:
- Image classification
- Object detection
- Object identification
- Object tracking
To train models from the scratch, you need considerable effort. Luckily, a good number of resources are available to ease your task. You can rely on Microsoft Common Objects in Context and ImageNet as resources for the training of new models. In your search, you can come across several pre-trained models such as facial recognition, object detection, and image classification.
Having an idea about the work process of a video analytics system will help you know what system you should take for your business such as a retail chain.