A New Human Eye Tracking Algorithm of Optimized TLD Based on Improved Mean-Shift

摘要

In this paper, an improved Mean-shift algorithm was integrated with standard tracking-learning-detection (TLD) model tracker for improving the tracking effects of standard TLD model and enhancing the anti-occlusion capability and the recognition capability of similar objectives. The target region obtained by the improved Mean-shift algorithm and the target region obtained by the TLD model tracker are integrated to achieve favorable tracking effects. Then the optimized TLD tracking system was applied to human eye tracking. In the tests, the model can be self-adopted to partial occlusion, such as eye-glasses, closed eyes and hand occlusion. And the roll angle can approach 90 degrees, raw angle can approach 45 degrees and pitch angle can approach 60 degrees. In addition, the model never mistakenly transfers the tracking region to another eye (similar target on the same face) in longtime tracking. Experimental results indicate that: (1) the optimized TLD model shows sound tracking stability even when targets are partially occluded or rotated; (2) tracking speed and accuracy are superior to those of the standard TLD and some mainstream tracking methods. In summary, the optimized TLD model show higher robustness, stability and better responding to complex eye tracking requirement.