What changes has machine vision brought to video surveillance?

  As a representative application of machine vision , video surveillance systems with cameras as the core have been widely used in various industries such as security, transportation, buildings, and industry. To understand what changes machine vision has brought to video surveillance, we can further analyze from several major application areas of machine vision.
Target recognition
Target recognition technology and stable tracking methods are one of the key factors in the development of machine vision. It has been widely used in many fields, such as fingerprint recognition, face recognition, iris recognition for identity confirmation, and license plate recognition in intelligent traffic management, motor vehicle detection, parking lot management, etc.
A target recognition system should have the ability to detect, classify, and identify targets in complex backgrounds and various weather conditions, so that targets can be continuously tracked in a targeted manner.
In recent years, target recognition technology has gradually moved from theoretical exploration and laboratory simulation to practical application. Its technical methods have also evolved from classic statistical pattern recognition to recognition methods based on knowledge, models, multi-sensor information fusion and artificial neural networks.
Object Tracking
Moving object tracking is the process of determining the position of the same object in different frames of an image sequence. Its main working method is to select good target features and use appropriate search methods. According to the matching principle, existing tracking methods are divided into tracking based on models, regions, features and active contours.
Model-based tracking
Model-based tracking establishes a model of the tracked target based on certain prior knowledge, and then updates the model in real time by matching the tracking template. There are three traditional expression methods for moving objects:
1. Line drawing method: The essence of the target movement is the movement of the main frame, so this expression method approximates each part of the object with straight lines.
2. Two-dimensional contour: The use of this expression method is related to the projection of the object in the image.
3. Three-dimensional model: Use three-dimensional models such as generalized elliptical cylinders and spheres to describe the structural details of objects. This method often requires matching three-dimensional models between related image frames to obtain a quantitative description of object motion. Therefore, more parameters need to be calculated, and the matching process is more computationally intensive.

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