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With the growth in available computing power, we see increasingly crowded virtual environments. In densely crowded situations, collisions are likely to occur, and the choice in collision detection technique can impact the perceived realism of a real-time crowd. This paper presents an investigation into the accuracy of human observers with regard to the recognition of collisions between virtual characters. We show the result of two user studies, where participants classify scenarios as 'colliding' or 'not colliding'; a pilot study investigates the perception of static images, whereas the main study expands on this by employing animated videos. In the pilot experiment, we investigated the effect of two variables on the ability to recognize collisions: distance between the character meshes and visibility of the inter-character gap. In the main experiment, we investigate the angle between the character paths and the severity of the (near) collision. On average, respondents correctly classified 72% (static) and 68% (animated) of the scenarios. A notable result is that the maximum uncertainty in determining existence of collisions occurs when the characters are overlapping and that there is a significant bias towards answering 'not colliding.' We also discuss differences in bias in the recognition of upper- and lower-body collisions. [ABSTRACT FROM AUTHOR]