Object detection and tracking is an essential module in airborne optoelectronic equipment, and its performance is directly related to the accuracy of object perception. Improved Siamese network tracking algorithms have produced excellent results for various challenging datasets recently, but most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will result in tracking drift and eventually cause tracking failure. To solve these problems, this paper proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection; the algorithm uses the contour template of the target instead of the bounding box template to reduce the background clutter interference. A branch is added to the Siamese network to improve the tiny-YOLOv3 object detection network, where K-means clustering is used to find the most suitable anchor box. An expansion module layer is introduced to expand the receptive field. Therefore, our proposed model increases the anti-occlusion ability of the system and improves the object recapture probability of airborne optoelectronic equipment. The results of a simulation of benchmark test data set and a flight dataset show that the improved model is especially suitable for tracking and recapture of moving objects in complex environments; in addition, it can better adapt to deformed or occluded objects in long-term tracking, which improves the system response time and adaptability.（LSJGMS1811）。