A deep apparent features and adaptive aggregation network-based multi-face tracking method: first, using a face recognition data set to train an adaptive aggregation network; next, using a convolutional neural network-based face detection method to obtain the position of a face, initializing a face target to be tracked, and extracting face features; then, using a Kalman filter to predict the position of each face tracking target in a next frame, and again locating the position at which the face is located in the next frame to extract the features of the detected face; finally, using the adaptive aggregation network to aggregate face feature sets in each tracked face target tracking trajectory, dynamically generating a face depth apparent feature fused with a plurality of frames of information, performing similarity calculation and matching with the face position and the feature thereof obtained by means of detection in a current frame in conjunction with the predicted position and the fused feature, and updating a tracking status. The described method may improve the performance of face tracking.
DEEP APPARENT FEATURES AND ADAPTIVE AGGREGATION NETWORK-BASED MULTI-FACE TRACKING METHOD