A Delay-Free and Robust Object Tracking Approach for Robotics Applications

Abstract

In many robotic applications there is the need for detecting and tracking moving and/or static objects while the robot moves, in order to interact with them. High quality detection methods require considerable computational time when the number of objects to be detected is high, or when operating within dynamic, real-world environments. Then, when an object detection result is available, it is referred to a previous frame and not to the current one. A method for obtaining delay-free detections is introduced in this present article. It consists of projecting a delayed detection onto the current frame by using a set of feature tracks generated by using the KLT (Kanade-Lucas-Tomasi) tracker. The proposed method is shown to improve detection accuracy when the tracked object is moving with respect to the camera. In addition, the method is able to detect and manage false detections and occlusions using statistical classifiers (Support Vector Machine) and the Viterbi algorithm (Viterbi, IEEE Trans. Inf. Theory 13(2), 260–269 1967). The method is validated in a person-following task, and compared against a part-based HOG person detector, and four performant tracking methods (Meanshift, Compressive Tracking, Tracking-by-detection with Kernels and Kernelized Correlation Filter). Additionally, the method is validated in two additional tasks: face tracking and car tracking. In all reported experiments, the proposed method obtains the best performance among all compared methods.