Multi object tracking (MOT) is a key research technology in the environment sensing system of automatic driving, which is very important to driving safety. Online multi object tracking needs to accurately extend the trajectory of multiple objects without using future frame information, so it will face greater challenges. Most of the existing online MOT methods are anchor-based detectors, which have many misdetections and missed detection problems, and have a poor effect on the trajectory extension of adjacent object objects when they are occluded and overlapped. In this paper, we propose a discrimination learning online tracker that can effectively solve the occlusion problem based on an anchor-free detector. This method uses the different weight characteristics of the object when the occlusion occurs and realizes the extension of the competition trajectory through the discrimination module to prevent the ID-switch problem. In the experimental part, we compared the algorithm with other trackers on two public benchmark datasets, MOT16 and MOT17, and proved that our algorithm has achieved state-of-the-art performance, and conducted a qualitative analysis on the convincing autonomous driving dataset KITTI.

optics Top plate Rase plate Rot tom plate Aps detector Cuve r plates CB baffle ABSTRACTAs the core of visual sensitivity via imaging, image processing technology, especially for star tracker, is mainly characterized by such items as image exposure, optimal storage, background estimation, feature correction, target extraction, iteration compensation. This paper firstly summarizes the new research on those items at home and abroad, then, according to star tracker's practical engineering, environment in orbit and lifetime information, shows an architecture about rapid fusion between multiple frame images, which can be used to restrain oversaturation of the effective pixels, which means star tracker can be made more precise, more robust and more stable.

No abstract