Multiple instance learning tracking based on Fisher linear discriminant with incorporated priors

Abstract

Traditional tracking-by-detection methods use online classifier to track object, and the classifier can be degenerated easily using self-learning process. The article presents a multiple instance learning (MIL) tracking method based on a semisupervised learning model with Fisher linear discriminant (MILFLD). First, the overlap rate of sampled instances and tracking object served as the prior information. Using both labeled and unlabeled data, the tracking drift problem in the learning model could be alleviated. Second, the lost function of MILFLD is built using Fisher linear discriminant model incorporated with priors. Hence the optimal classifier can be selected out directly in instance level. Last but not least, the classifiers are chosen by gradient descent method, assuring the maximum descent of lost function. Therefore, the classifiers selected at previous frames are still discriminative to future frames, which can help to constrain the error propagation. Comparison experiments show that the center location errors of online AdaBoosting , online MIL tracking, weighted MIL tracking (WMIL), compressive tracking (CT), struck tracking, and MILFLD are 78, 66, 62,74, 59, and 25 pixels, respectively, which demonstrates the tracking accuracy of our method. The experiments of robot motion tracking in realistic scenario have been complemented for comparison as well. Despite the variations in illumination, deformation, or occlusions of the objects, the proposed method can track the target accurately and has high real-time performance.

Trajectory planning method for improving alignment accuracy of probes for dual-robot air-coupled ultrasonic testing system

“…The trajectory data need to be postprocessed with equations (5) to (16). This process constraint the X-axis and convert the normal vector direction cosines to the Euler rotation angles to fit the robot control requirement.…”

“…His algorithm minimized the computational time without compromising the accuracy of the end effector, and he also proposed a practical trajectory planning method for mobile robot. 15,16…”

Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning

“…Zhou et al [48] improved the MIL tracking algorithm by optimizing a bag Fisher information function and integrating the co-training criterion. And, Zhou et al [49] also proposed a multiple instance learning (MIL) tracking method based on a semi-supervised learning model with Fisher linear discriminant. However, in the above methods [6,16,47,48,49], the positive bag may contain some negative samples because the radius of the positive bag is difficult to be very accurately selected.…”

“…And, Zhou et al [49] also proposed a multiple instance learning (MIL) tracking method based on a semi-supervised learning model with Fisher linear discriminant. However, in the above methods [6,16,47,48,49], the positive bag may contain some negative samples because the radius of the positive bag is difficult to be very accurately selected. erefore, when all the instances in the positive bag are used to update the classifiers, the above algorithms might suffer from the drifting problem in the complex environments.…”

Robust tracking control of an underwater vehicle and manipulator system based on double closed-loop integral sliding mode

“…With the rapid development of artificial intelligence, several scholars have tried to apply more intelligent methods to the motion control of robots. [26][27][28][29][30] Xu et al 31 proposed a new type of robust control method, constructing sliding mode surface function to control the tracking error of UVMS and adjusting the gain of the nonlinear control based on the fuzzy control method. In addition, the fuzzy logic is employed to regulate the controller's gain in real time, so that the controller does not need an accurate UVMS dynamic model, and ensures excellent tracking quality and stability with external current disturbances.…”