Gaole Wei

Bio: Gaole Wei is an academic researcher. The author has contributed to research in topics: Scale space. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Aerial infrared target tracking method based on KCF for frequency-domain scale estimation.

Abstract: The kernel correlation filter (KCF) tracking algorithm encounters the issue of tracking accuracy degradation due to large changes in scale and rotation of aerial infrared targets. Therefore, this paper proposes a new scale estimation KCF-based aerial infrared target tracking method, which can extract scale feature information of images in the frequency domain based on the distribution characteristics and change laws of frequency-domain energy. In addition, the proposed method can improve the accuracy of target scale information estimation. First, the KCF tracking algorithm is used to obtain the target position. Then, spectral eigenvalues are calculated as eigenvectors, and frequency-domain rotation scale invariance is adopted to extract the eigenvector between two frames as the target rotation change information. Reverse rotation is performed on the current frame spectrum map for isolating the effects of target rotation on scale information estimation. Then, the current target scale is estimated on the basis of the eigenvectors between the adjacent frames. Finally, the length-to-width ratio and the scale of the tracking box are updated on the basis of the target rotation information, which improves the adaptability of the tracking box to changes in the target scale and rotation. The results indicate that the proposed algorithm is suitable for stable tracking of target scales and rapid changes in attitudes. The average tracking accuracy and the average success rate of the algorithm are 0.954 and 0.782, which represent improvements of 5.3% and 18.9%, respectively, compared with the KCF algorithm. The average tracking success rate is improved by 4.1% compared with the discriminative scale space tracker algorithm, and the average tracking performance is better than that of related filter tracking algorithms based on other scale estimation methods.

An adaptive optical flow technique for person tracking systems

Abstract: Optical flow can be used to segment a moving object from its background provided the velocity of the object is distinguishable from that of the background, and has expected characteristics. Existing optical flow techniques often detect flow (and thus the object) in the background. To overcome this, we propose a new optical flow technique, which only determines optical flow in regions of motion. We also propose a method by which output from a tracking system can be fed back into the motion segmenter/optical flow system to reinforce the detected motion, or aid in predicting the optical flow. This technique has been developed for use in person tracking systems, and our testing shows that for this application it is more effective than other commonly used optical flow techniques. When tested within a tracking system, it works with an average position error of less than six and a half pixels, outperforming the current CAVIAR1 benchmark system.

A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter.

Abstract: To meet the challenge of video target tracking, based on a self-organization mapping network (SOM) and correlation filter, a long-term visual tracking algorithm is proposed. Objects in different videos or images often have completely different appearance, therefore, the self-organization mapping neural network with the characteristics of signal processing mechanism of human brain neurons is used to perform adaptive and unsupervised features learning. A reliable method of robust target tracking is proposed, based on multiple adaptive correlation filters with a memory function of target appearance at the same time. Filters in our method have different updating strategies and can carry out long-term tracking cooperatively. The first is the displacement filter, a kernelized correlation filter that combines contextual characteristics to precisely locate and track targets. Secondly, the scale filters are used to predict the changing scale of a target. Finally, the memory filter is used to maintain the appearance of the target in long-term memory and judge whether the target has failed to track. If the tracking fails, the incremental learning detector is used to recover the target tracking in the way of sliding window. Several experiments show that our method can effectively solve the tracking problems such as severe occlusion, target loss and scale change, and is superior to the state-of-the-art methods in the aspects of efficiency, accuracy and robustness.