In the evolving tech landscape, various container technologies coexist and offer compelling advantages in quickly deploying applications and efficiently utilizing resources on edge devices. Despite the potential benefits of containerization in general, limited research has explored how various container technologies perform in specific domains. In response, this paper provides an extensive evaluation of container technologies (e.g., RunC, LXC, Containerd, Docker, Podman, and Singularity) in the context of OpenCV-based computer vision applications on ARM-based edge devices. Experiments verify that the performance of containerized computer vision applications is comparable to that of non-containerized ones. While the performance is roughly equivalent across all container runtimes/engines, Docker consistently demonstrates superior efficiency for computer vision applications on ARM-based edge devices. These insights contribute to bridge the existing gap to the integration of containers in IoT and ARM-based edge computing scenarios.