Resumen
To address the issue of poor tracking accuracy and the low recognition rate for multiple small targets in infrared images caused by uneven image intensity, this paper proposes an accurate tracking algorithm based on optical flow estimation. The algorithm consists of several steps. Firstly, an infrared image subspace model is established. Secondly, a full convolutional network (FCN) is utilized for local double-threshold segmentation of the target image. Furthermore, a target observation model is established using SIR filtering particles. Lastly, a shift vector sum algorithm is employed to enhance the intensity of the infrared image at a certain time scale in accordance with the relationship between the pixel intensity and the temporal parameters of the detected image. Experimental results demonstrate that the multi-target tracking accuracy (MOTA) reaches 79.7% and that the inference speed frame per second (FPS) reaches 42.3. Moreover, the number of ID switches during tracking is 9.9% lower than that of the MOT algorithm, indicating high recognition of cluster small targets, stable tracking performance, and suitability for tracking weak small targets on the ground or in the air.