Object tracking is an important research area in computer vision and has been widely adopted both in military and civilian applications. Improving tracking accuracy and stability in realistic scenarios that involve appearance change

occlusion

and illumination change is still difficult for practical application. A tracking method based on the phase congruency transformation and metric learning was presented to solve the aforementioned problem. This study formulates object tracking as a matching task to find a candidate

which is most similar to the target model

over the subsequent image frames. This process is largely controlled by two factors:the selected features that characterize objects and the distance metric used to determine the closest match in the selected feature space. First

the features were extracted by phase congruency transformation. Combining the advantages of ensemble learning and support vector machine (SVM)

we then introduce a type of ensemble metric learning to obtain a distance metric matrix utilizing a small number of training data extracted from the fore sequence of images. Most approaches directly solve the optimal metric matrix and induce a large increase in the calculation as the feature dimension increases. In contrast

our method indirectly obtains the projection matrix by learning multiple projection vectors; thus

it is simple and efficient even with high-dimension features. Candidates are obtained by Markov chain Monte Carlo sampling and calculate the distance from the target model utilizing the learned metric matrix in the tracking process. The candidate with the smallest distance value is regarded as the target. Moreover

the object model and metric matrix are constantly updated with new training data extracted during tracking for adaptability. The effectiveness of the algorithm has been verified on several challenging video sequences that contain a dynamic background

appearance changes

and occlusions. The AEMTrack algorithm proposed in this study is clearly smaller on both the mean and standard deviation of the location error than those from three mainstream methods. Together with the quantitative assessment of tracking a successful rate

experimental results show that the accuracy of our proposed method even exceeds several mainstream methods in existing tracking studies and has appropriate stability. This study designs and realizes a new tracking method based on metric learning. A metric matrix is learned and tends to maximize the distance between samples of different classes using a small amount of training data sampled from an image sequence during tracking. The metric learning process is decomposed into multiple independent linear SVM

which can be executed in parallel implementation. This method can also result in dimension reduction; thus

it is efficient even in high-dimensional space. New targets and background samples are also applied to update the model in the tracking process; hence

the algorithm is adaptive. This tracking method has suitable generality because no limitation on the feature dimension of the target exists. Experimental results show that the proposed method can obtain an accurate and stable tracking effect in the complex scene

including appearance and illumination changes.