Citation: | He Junheng, Liu Shu, Di Hongwei. TLD target tracking algorithm based on dynamic capture[J]. Opto-Electronic Engineering, 2018, 45(8): 180030. doi: 10.12086/oee.2018.180030 |
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Overview: The target tracking technology based on computer vision is widely used in civil and military fields, such as traffic monitoring, security monitoring, uav tracking and human-computer interaction. However, target tracking is hardly applied to the actual scene. The main reasons are as followed. To begin with, target may suffer from deformation, illumination variation and background clutter during the tracking, which will make the tracking system lose the target. What's more, target may be vague due to the fast motion. Last but not least, target may be blocked by something. In addition, many target tracking algorithms are too complicated to complete real-time tracking.
The tracking-learning-detection algorithm is a new single-target long time tracking algorithm proposed by Zdenek Kalal. The tracking-learning-detection (TLD) algorithm is different with the algorithm which is based on the conventional tracking algorithm. The TLD algorithm combines the tracking algorithm and the detection algorithm to solve the problem about the shape change, fast moving and partial shade of the target during the tracking. At the same time, the target model and related parameters of tracking module and detection module are constantly updated through online learning module, which makes the tracking effect more stable, robust and reliable. However, TLD target tracking algorithm has high complexity and it is difficult to achieve the real-time effect of target tracking. Because the TLD algorithm has good robustness and is suitable for application to actual tracking process, this paper studies how to improve and optimize the TLD algorithm.
By analyzing the characteristics of the TLD algorithm, this paper proposes DC-TLD target tracking algorithm based on dynamic capture region. Firstly, by changing the target location prediction method, the position of the previous frame is used to predict the position of the target in the current frame, which will reduce the detection time of the target sample. Secondly, the sample selection method is improved to obtain enough positive and negative samples in the small sample selection. Thirdly, the sample access method is improved by numbering the sample, and the access speed of the sample is improved by index access. The above measures successfully reduce the complexity of the TLD target tracking algorithm and improve the robustness of the algorithm, which make the algorithm closer to real-time requirements. However, when the tracking target is large, the tracking effect is similar as DC-TLD algorithm and TLD algorithm. And it is hard for DC-TLD to track multiple targets.
TLD algorithm framework[5]
DC-TLD algorithm framework
BlurFace target location prediction and error
Jumping target location prediction and error
Critical point analysis diagram
R/r and tracking success rate, failure rate, negative sample size relationship
R/r and Q relations
Crowds test set algorithm running time per frame
BlurFace test set algorithm running time per frame
Video sequence test diagram.