Directional solidification experiments of transparent alloy systems typically show multiple dendrites, a forest of dendrites, growing with preferential alignment. At the length scale of centimetres, an experiment could have hundreds of observable dendrites. Analysis of every dendrite would be laborious and practically difficult to implement. Hence, low numbers of dendrites are routinely selected for analysis as they are assumed to be representative of the growth conditions. Hence, many dendrites go without being analysed. Here, a bespoke experimental apparatus with a novel computer vision algorithm is presented that automatically detects and simultaneously tracks multiple columnar dendrite tips from in-situ video data of directional solidification. The benefits of the algorithm are demonstrated with an application to an experimental test case with the transparent alloy system Neopentyl Glycol-35 wt%D-Camphor (NPG-35 wt%DC). Comparisons of dendrite tip velocity and undercooling measurements with microgravity experimental results from the literature showed notable differences. The current terrestrial data showed similar growth rates but at lower undercoolings (by factors in the range of 0.41–0.68) to that measured in the microgravity experiments. Comparisons were made to the classical Lipton-Glicksman-Kurz (LGK) model and to a modified LGK model adapted with a finite diffusional boundary layer theory to account for convection effects. The modified LGK model showed good agreement for boundary layers between 2.5 and 7.0 µm. An oscillatory component to the tip velocity was observed between adjacent columnar dendrites. Video data of columnar dendritic growth augmented with tip velocity vectors are presented. The tip tracking algorithm is beneficial as, with 385 dendrite tips tracked, it provides statistical and qualitative insights that are otherwise difficult to reconcile using traditional methods.