Instruction vs. Deduction: Deep Learning and Advances in VCA
Deep Learning is changing the industry
As camera counts and the data they provide grow ever-larger, it becomes increasingly difficult for organizations to monitor, perform investigations, and draw useful conclusions from the valuable information gathered by their video surveillance infrastructure.
Video analytics have long been seen as a technology solution to help identify activity and information from all the video data. Video analytics have largely fallen short of delivering on that market expectation. However, Deep Learning may change that. But what is Deep Learning, and how can it improve on conventional techniques?
Machine Learning Techniques and VCA
Most Video Content Analytics (VCA) developed to-date have been based on traditional, algorithmic, Machine Learning techniques. Deep Learning is a more advanced evolution of machine learning, using sophisticated, artificial neural networks.
In the context of VCA, both Machine Learning and Deep Learning instruct software to develop a model of objects based on a variety of attributes the software “learns” about those objects. The model helps the software to later identify and categorize an object in the video feed which matches those attributes the software has learned. For instance, an object moving through the camera’s field of view may be taller than it is wide, as opposed to another object, which is wider than it is tall. The VCA software may classify the first object as a person and the second as a vehicle, based on those attributes.
In reality, multitudes of data points are used to classify objects, but some attributes are more important than others. The VCA software will weigh the various criteria it uses to classify objects in order to determine the probability that an object is more likely to be a person, vehicle or something else. Once an object enters the scene, the object is analyzed, and its properties are measured. To determine what an object is, the VCA may begin by looking at the object’s dimensions, color variation, and movement patterns.