Real-Time Detection Algorithms for multiple types of objects in Complex Backgrounds

By collecting massive image and video samples offline, the feature value is extracted after the samples are manually calibrated and trained based on the feature value and sample calibration to design an efficient feature selection classifier. In a real-time system, feature selection classifier is used to detect, track, and identify various objects, such as pedestrians, motor vehicles, and non-motor vehicles.

In a complex contextAlgorithmIt can provide precise target objects for subsequent complex segmentation and recognition tasks, saving resources and improving efficiency.

Advantages:

It can detect multiple types of objects at the same time, such as faces, human bodies, car bodies, license plates, vehicle logos, traffic signs, industrial parts, and other objects of interest;

The imaging angle conditions have little impact on the detection results, and can detect target objects with multiple attitudes and angles.