Due to the complexity of background and diversity of small targets, robust detection of infrared small targets for the trajectory correction fuze has become a challenge. To solve this problem, different from the traditional method, a state-of-the-art detection method based on density-distance space is proposed to apply to the trajectory correction fuze. First, parameters of the infrared image sensor on the fuze are calculated to set the boundary limitations for the target detection method. Second, the density-distance space method is proposed to detect the candidate targets. Finally, the adaptive pixel growth (APG) algorithm is used to suppress the clutter so as to detect the real targets. Three experiments, including equivalent detection, simulation and hardware-in-loop, were implemented to verify the effectiveness of this method. Results illustrated that the infrared image sensor on the fuze has a stable field of view under rotation of the projectile, and could clearly observe the infrared small target. The proposed method has superior anti-noise, different size target detection, multi-target detection and various clutter suppression capability. Compared with six novel algorithms, our algorithm shows a perfect detection performance and acceptable time consumption.

In outdoor sports such as alpine skiing, athletes hope to obtain information such as movement trajectory and speed to achieve scientific training. The portable sensor has a sense of restraint and affects the athlete's performance. Cameras have difficulty capturing the whole race in an outdoor environment. For alpine skiing needs, we designed an alpine skiing information acquisition system, using unmanned aerial vehicle (UAV) and ground cameras to obtain information on the whole path of the race. The environment of the alpine skiing field, the high-speed curvilinear movement of athletes and the flight characteristics of UAVs cause challenges in the detection and tracking of athletes by UAVs. To realize the detection and tracking of alpine skiing by UAVs, we propose a detection algorithm by combining neural networks and correlation filters. A tracking confidence score based on the motion distance of the interval frame is defined. When the tracking confidence of the neural network detector is less than the threshold, the correlation filter is used to expand the recognition range and realize redetection. In addition, we created and annotated a fresh alpine skiing dataset. We compare our algorithm with four advanced algorithms on the UAV123 dataset. Three methods are used to evaluate the performance of our method on the alpine skiing sequence dataset. From the simulation results, our algorithm outperforms the comparison methods in terms of accuracy and robustness. Therefore, our algorithm has application value in the scientific training of alpine skiers.INDEX TERMS Alpine skiing, dual tracker, intelligent sports, object detection, target tracking.

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Technical motion recognition in cross-country skiing can effectively help athletes to improve their skiing movements and optimize their skiing strategies. The non-contact acquisition method of the visual sensor has a bright future in ski training. The changing posture of the athletes, the environment of the ski resort, and the limited field of view have posed great challenges for motion recognition. To improve the applicability of monocular optical sensor-based motion recognition in skiing, we propose a monocular posture detection method based on cooperative detection and feature extraction. Our method uses four feature layers of different sizes to simultaneously detect human posture and key points and takes the position deviation loss and rotation compensation loss of key points as the loss function to implement the three-dimensional estimation of key points. Then, according to the typical characteristics of cross-country skiing movement stages and major sub-movements, the key points are divided and the features are extracted to implement the ski movement recognition. The experimental results show that our method is 90% accurate for cross-country skiing movements, which is equivalent to the recognition method based on wearable sensors. Therefore, our algorithm has application value in the scientific training of cross-country skiing.