Visual perception， as one of the core technologies of environmental understanding， provides accurate environmental information for intelligent mobile systems （such as autonomous driving） and is an important prerequisite for ensuring safety decisions. 3D object detection technology based on Bird’s Eye View （BEV） has become the mainstream paradigm in the field of environmental perception because of its efficiency and accuracy. To further promote the research of 3D object detection algorithms based on BEV， the following was performed. Firstly， the BEV 3D object detection algorithms were classified systematically， and according to the modals of the input data， they were divided into three categories： pure camera algorithms， pure LiDAR algorithms and camera-LiDAR fusion algorithms. Secondly， the role of pre-training algorithms in improving detection performance was explored. Thirdly， the advantages and disadvantages of the algorithms fusing temporal features in dynamic scenarios and the performance of the algorithms fusing height features in complex environments were analyzed. Fourthly， the breakthrough progress made by large model collaborative BEV object detection in object detection accuracy and scenario understanding was sorted out. Finally， the core conclusions of BEV 3D object detection algorithms were summarized， and future research directions were looked forward， so as to provide new ideas for research work in this field.

In the scenario of using Unmanned Aerial Vehicle (UAV) as the data collector for computation offloading to provide Mobile Edge Computing (MEC) services to User Equipment (UE), a wireless communication strategy to achieve efficient UE coverage through UAV was designed. Firstly, under the condition of a given UE distribution, for the UAV flight trajectory and communication strategy, an optimization method of Successive Convex Approximation (SCA) was used to obtain an approximate optimal solution that was able to minimize the global energy. In addition, for scenarios with large-scale distribution of UEs or a large number of tasks, an adaptive clustering algorithm was proposed to divide the UEs on the ground into as few clusters as possible, and to ensure the offloading data of all UEs in each cluster was able to be collected in one flight. Finally, the computation offloading data collection tasks of the UEs in each cluster were allocated to one flight, so that the goal of reducing the number of dispatches required for a single UAV or the UAV number of dispatches required for multiple UAVs to complete the task was achieved. The simulation results show that the proposed method can generate fewer clusters than the K-Means algorithm and converge quickly, and is suitable for UAV-assisted computation offloading scenarios with widely distributed UEs.

Concerning the problem that pedestrians would be partially or seriously shaded by each other in video monitoring, this paper proposed a people counting algorithm based on human body skeleton feature. At first, the initial human skeleton was extracted by morphological skeleton extraction algorithm. Then the optimal skeleton feature was obtained by eliminating outliers and pseudo branches. Finally, this paper established a head detection response rule through analyzing the characteristics of skeleton in head areas to detect the head of pedestrian, and completed people counting by counting the heads of pedestrians. The experimental results show that the algorithm can solve the problems of partial and serious shading in video monitoring. For relatively sparse scene, the overall people counting accuracy rate of the algorithm is about 95%.