The way image sequences are encoded by technological systems, that is video, is fundamentally tied to the way in which the human eye and brain interpret images and motion. This includes such aspects as resolution, colour, dynamic range, frame rates and spatial and temporal compression techniques. On the contrary, object identification algorithms are commonly based on single image analysis, such as the extraction of a single video frame from a sequence. This mismatch of, in particular, temporal processing paradigms means that most object analysis algorithms are not well suited to the data with which they are presented. In order to bridge this gap we investigate the temporal preconditioning of video data through a biologically-inspired vision model, based on multi-stage processing analogous to the vision systems of insects. In doing so, we argue that such an approach can lead to improved object identification through the enhancement of object perimeters and the amelioration of lighting and compression artefacts such as shadows and blockiness.