Abstract: The disclosure provides a multi-object tracking algorithm based on an object detection and feature extraction combination model, including the following steps: S1, adding an object appearance feature extraction network layer behind a prediction feature layer of an object detection tracking network having an FPN structure; S2, calculating object fused loss of the object detection tracking network having the FPN structure and added with the object appearance feature extraction network layer; S3, forming a feature comparison database utilizing a neural network during multi-frame objection detection and tracking process; and S4, comparing current image object appearance features with features in the feature comparison database, drawing an object trajectory if the objects are uniform; else adding the current image object appearance features into the feature comparison database to form a new feature comparison database, and then repeating steps S2 and S3.

Abstract: The disclosure provides a feature compression algorithm based on neural network, including the following steps: S1, image data preparation: collecting facial images, and uniformly performing map processing to the facial images collected; S2, feature data acquisition: delivering the facial images processed into a face recognition system for face detection and feature extraction, and saving facial feature data; S3, setting up a neural network model; S4, model iteration training; S5, storing a parameter model; and S6, feature compression. The feature compression algorithm based on neural network of the disclosure can not only achieve compression of original feature data, but also retain its original semantic feature, which belongs to a higher-dimensional feature abstraction. The compressed feature data can be directly used.

Abstract: The disclosure provides a feature compression algorithm based on neural network, including the following steps: S1, image data preparation: collecting facial images, and uniformly performing map processing to the facial images collected; S2, feature data acquisition: delivering the facial images processed into a face recognition system for face detection and feature extraction, and saving facial feature data; S3, setting up a neural network model; S4, model iteration training; S5, storing a parameter model; and S6, feature compression. The feature compression algorithm based on neural network of the disclosure can not only achieve compression of original feature data, but also retain its original semantic feature, which belongs to a higher-dimensional feature abstraction. The compressed feature data can be directly used.