Car park video surveillance data provides plenty of semantic rich data such as vehicle color, trajectory, speed, and type which can be tapped into and extracted for video and data analytics. We present methods for extracting and retrieving color and motion semantics from long term carpark video surveillance. This is a challenging task in outdoor scenarios due to ever-changing illumination and weather conditions, while retrieval time also increases as data size grows. To address these challenges, we subdivided the search space into smaller chunks by introducing spatio-temporal cubes or atoms, which can store and retrieve these semantics at ease. The proposed method was tested on 2, days of continuous data from an outdoor carpark under various lighting and weather conditions. We report the precision, recall and F1 scores to determine the overall performance of the system.