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The pedestrian-following robot aims to robustly maintain a standard distance from a specific pedestrian in an unknown complex environment, which is challenging when facing the limited field of view (FOV) and the computing resource constraints. However, the pedestrian-following robot commonly utilizes the long range dependence of targets to construct tracking models via the transformer tracking method, which is often insufficient to achieve real-time following. To address this important issue, we propose a new fast tracking network for the following robot, including a lightweight target detector, a target state prediction decoder, and an adaptive visual servo controller. First, in the lightweight target detector, a dense feature extraction process is designed by stacking depthwise group-separable convolutions. A context encoder is also developed, and is coupled with the proposed person re-identification (Re-ID) branch to detect multiple targets with high precision and speed. Second, in the target state prediction decoder, we introduce a flexible multiattention mechanism to obtain target Re-ID features from the previous frame for predicting the target's position in the current frame. Third, in the adaptive visual servo controller, we design a six-parameter dynamic model for the proportional integral derivative (PID) controller to stably follow the pedestrian under the limited FOV. Extensive experimental results demonstrate that the proposed method is efficient and accurate. Moreover, it also exhibits strong robustness and high real-time performance in unknown complex environments.

In complex tracking scenarios of multitarget occlusion, especially when the target is seriously occluded, feature loss can lead to tracking failure. In this article, we develop a multitarget occlusion tracking algorithm based on a 3-D spatio-temporal context graph model, which can fully utilize the targets’ RGB-D information. Specifically, we first extract target’s 3-D features containing appearance, motion, spatial, and temporal features. Then, the spa’Io-temporal context features between different targets are also be acquired. Furthermore, the attribute relational graph (ARG) model with target’s 3-D spatio-temporal context features is constructed, which combines spatial features in the current frame and temporal features between consecutive frames. Moreover, if the target is determined to be occluded according to the matching results in the current frame, the probability relaxation method is used to assign the optimal tracking label to clustering blocks in occlusion area by matching these ARG models, which can address the multitarget cross occlusion problem. Extensive experiments on the Princeton Tracking Benchmark Datasets clearly demonstrate that our method can achieve superior performance against the typical algorithms proposed in recent years.

Background: In the face of the development trend of high-end manufacturing servitization, the reliability standard of manufacturing products gradually increases. Objective: In order to accurately predict the product life cycle, the accelerated degradation evaluation technology could be applied to significantly shorten the experiment duration. As the technologies of intelligent manufacturing and industrial big data develop, the theory of accelerated degradation evolves as well. Methods: Based on the scientific knowledge mapping, co-author network and co-existence network, 22283 pertinent articles since the year 2010 have been collected to conduct a bibliometric study. Results: The results show that the accelerated degradation reliability assessment spans over many research fields, and achieves great development in the mathematical modeling and experiment verification. Conclusion: To further the study, more efforts are expected in the areas such as building effective evaluation systems and enhancing the credibility of the assessment outcomes, as more advanced sensory data and wireless communication technologies become available.