Computer vision application is a broad-spectrum sophisticated technique of high accuracy, which can be used efficiently to detect objects from a video footage. The principle of feature detection is being implemented through this technique for the recognition of desired behavioural patterns. Our results demonstrate a competent function of SURF (Speeded Up Robust Features), a computer vision algorithm in assessing animal behaviour from a recorded video. In the present study, we have applied this algorithm for accurate quantification of grooming behaviour in aquatic arthropods, using a semi-transparent freshwater prawn, Macrobrachium lamarrei as a model organism. Grooming behaviour is considered as an index of neuronal stress in several animals. We predict the effective application of this method in the area of behavioural ecology and neuroethological research in diverse group of animals.