Visual surveillance systems have been playing an important role in monitoring and managing at public areas. However, the computational complexity of video compression in these applications is still a great challenge. To meet practical requirements, the authors propose in this paper a low-complexity surveillance video coding solution in which the most recent Versatile Video Coding (VVC) standard is improved with a novel learning adaptive motion search algorithm. The proposed algorithm is designed based on the temporal motion and spatial texture characteristics of surveillance videos. First, the authors study and define a list of spatial and temporal features which indicates the motion and texture characteristics of surveillance video. These features are used together with a machine learning algorithm to appropriately assign a search range for the VVC motion search. Second, to reduce search points, the authors propose an adaptive Test Zone (TZ) search in which TZ steps are early terminated following the variation of spatial–temporal features. Performance evaluation conducted for a rich set of surveillance videos and relevant benchmarks have shown the superiority of the proposed method, notably with around 33% of encoding time saving when compared with the state-of-the art VVC solution and relevant benchmarks while asking for negligible compression loss