Video surveillance has been widely employed in our society in the past years. In this context, humans play an important role and are the major players since they are responsible for changing the state of the scene through actions and activities. Therefore, the design of automatic methods to understand human behavior and recognize activities are important to determine which subjects are involved in an activity of interest. The computer vision research area has contributed vastly for the development of methods related to detection, tracking and recognition of humans. However, there is still a lack of methods able to recognize higher level activities (e.g., interaction among people that might be involved in an illegal activity). The first step to be successful in this enterprise is to detect and locate groups of people in the scene, which is essential to make inferences regarding interactions among persons. Aiming at such direction, this paper presents a group detection approach that combines motion and spatial information with low-level descriptors to be robust to situations such as partial occlusions. The experimental results obtained using the PETS 2009 and the BEHAVE datasets demonstrate that the proposed combination indeed achieves higher accuracies, indicating a promising direction for future research.