Abstract: The research of infrared object intelligent detection and tracking technology is always a hot topic in the same field,especially in precision guidance,sea surface surveillance and sky warning.Aiming at the problems that tracking accuracy is reduced due to ground miscellaneous interference,multi-object block interference,platform shaking and other complex scenes,an infrared ground multi-object tracking method based on improved ByteTrack algorithm is proposed.First of all,a modified Kalman filter which could adaptively modulate the noise scale is introduced to alleviate the impact of low-quality detection on vanilla Kalman filter.Secondly,the introduction of enhanced correlation coefficient maximization is used to settle the inter-frame images to compensate the platform shaking impact.Then ByteTrack increases the motion model based on long short-term memory network to solve the prediction error caused by Kalman filter in the non-linear motion state.Finally,two lightweight offline algorithms of link model and Gaussian-smoothed interpolation are introduced to refine the tracking results.Experiment is performed on the infrared ground multi-object dataset and the results show that compared with Sort and DeepSort,the MOTA of the improved algorithm increases by 8.3% and 10.2%,IDF1 increases by 6.5% and 5.6%,respectively.The improved algorithm shows better effectiveness and will be used in the infrared object intelligent detection and tracking scenes.

Key words: Multi-object tracking, Infrared object, ByteTrack, Kalman filter, Long short-term memory network