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.
MULTI-OBJECT TRACKING ALGORITHM BASED ON OBJECT DETECTION AND FEATURE EXTRACTION COMBINATION MODEL